Multiple Clinical Variables Research Articles

Conflict at work and incident chronic low back pain: a retrospective cohort of more than 101,000 adults from Germany.

Little is known about the relationship between conflict at work and incident chronic low back pain (CLBP). Thus, this retrospective cohort study analyzed the association between conflict at work and the five-year incidence of CLBP in adults living in Germany. This study included individuals aged 18-65 years reporting conflict at work for the first time in one of 1,293 general practices in Germany between 2005 and 2022 (index date). Patients not reporting conflict at work were matched (5:1) to those reporting conflict at work using a propensity score based on multiple demographic and clinical variables. In people not reporting conflict at work, the index date was a randomly selected consultation in 2005-2022. CLBP corresponded to two diagnoses of LBP at least three months apart. The relationship between conflict at work and incident CLBP was studied using Cox regression models. There were 16,925 patients reporting (mean [SD] age 39.5 [13.0] years; 63% women) and 84,625 patients not reporting conflict at work (mean [SD] age 39.6 [12.9] years; 64% women). Conflict at work was not associated with incident CLBP in the overall population (HR = 1.07, 95% CI = 0.99-1.14). However, there was a positive and significant association for CLBP without sciatica, individuals aged 18-30 years, and those with no history of low back pain before the index date. Conflict at work may be a predisposing factor for CLBP without sciatica, highlighting the need to prevent conflict in the workplace. Further research is warranted to corroborate these results in other countries.

Development of brain metastases in non-small-cell lung cancer: high-risk features.

Aim: Brain metastases (BM) are a common site of disease progression and treatment failure in non-small-cell lung cancer (NSCLC) and can be identified in up to 30-50% of patients. Although they are common, there is no standardized screening protocol for development of BM in NSCLC. Multiple clinical variables predict increased BM occurrence, and, when present, should be used to initiate screening MRI.Materials & methods: We performed a single center retrospective review of NSCLC patients, examining BM development and overall survival. Available clinical, radiographic and molecular data were reviewed for association with BM and overall survival. A predictive model for BM development was created for multivariate analysis.Results: Risk factors for new BM development in NSCLC included younger age, larger primary lung tumor, Karnofsky performance score (KPS) <70, pre-existing liver or bone metastases, large cell histology and family history of cancer. Factors associated with decreased OS were larger primary lung tumor, extracranial metastases at time of diagnosis, large cell histology and poorly-differentiated carcinoma histology.Conclusion: There are multiple high risk features for developing BM in NSCLC. Each of these factors should routinely be investigated, and presence should prompt brain MRI to allow earlier diagnosis and treatment of BM.

Factors affecting survival of foals with pneumonia in a referral hospital

BackgroundPneumonia is a common condition in ailing neonatal foals, and it remains an important cause of morbidity and mortality in this veterinary patient group. Factors affecting the survival of young foals with pneumonia have not been thoroughly investigated. The aim of this study was to explore the potential prognostic factors associated with survival of these foals. Fifty foals under one month of age with pneumonia were included in this retrospective clinical study. The foals were divided into groups based on survival (survived to discharge or died/euthanised during hospitalisation). Multiple clinical and laboratory variables were investigated as risk factors with univariate logistic regression analyses and subsequently with multivariate analyses. If a variable showed prediction potential in regression analysis, a receiver operating characteristic (ROC) analysis was conducted.ResultsIn univariate analysis, odds (OR, 95% CI) of non-survival were associated with higher respiratory rate (RR) on the first day after admission to hospital (D1) (1.32, 1.07–1.62, P = 0.009, for each 5-unit increase) and positive bacterial blood culture (12.08, 1.88–77.67, P = 0.009). Odds of non-survival were decreased for Standardbred breed (0.11, 0.01–0.96, P = 0.046) and for foals with longer hospitalisation, with each additional day in hospital further reducing the odds (0.59, 0.40–0.86, P = 0.006). In multivariate analysis, odds of non-survival were associated only with higher RR on D1 (1.36, 1.07–1.71, P = 0.011, for each 5-unit increase). In ROC analysis, optimal cut-off value for RR was ≥ 55/min with sensitivity 75.0% and specificity 76.3%. Based on predictive values, RR < 55/min on D1 favoured survival.ConclusionsHigher RR on D1 is a predictor of non-survival in foals with pneumonia in this study, increasing the odds of death by 36% for each 5-unit increase in RR. Respiratory rate below 55/min on D1 favours survival. These findings could assist in early identification of foals that are at increased risk of mortality, thereby aiding in treatment decisions.

Associations of iron deficiency with cardiac function, congestion, exercise capacity and prognosis in heart failure.

Uncertainty exists about defining true iron deficiency (ID) in heart failure (HF) patients. We assessed the relationship of different ID definitions with cardiac structure and function, congestion, exercise capacity, and prognosis in HF outpatients. Iron deficiency was defined according to guidelines (G-ID: ferritin <100 ng/ml or ferritin 100-299 ng/ml with transferrin saturation [TSAT] <20%). Alternative ID definitions based on TSAT (<20%), iron (≤13 μmol/L), and ferritin (<100 or < 300 ng/ml) were explored. Relationships with rest/exercise measures of cardiac function and congestion using ultrasound, effort intolerance and adverse outcome (HF hospitalizations or all-cause mortality) were assessed. Of 1502 patients (72% with left ventricular ejection fraction [LVEF] ≥50%), 471 (31%) had TSAT <20%, while 728 (48%) had G-ID. Patients with TSAT <20% or G-ID had greater left atrial volume but similar LVEF. Lower TSAT, iron and haemoglobin, but not ferritin, were associated with more signs of congestion by ultrasound. After correcting for multiple clinical variables, including haemoglobin, TSAT was directly associated with peak oxygen uptake (standardized coefficient 0.069, p = 0.041), while ferritin was not. There was no interaction with HF phenotype (HF with preserved vs. reduced LVEF). During a median follow-up of 18 months, TSAT <20% and iron ≤13 μmol/L were associated with worse outcomes in models adjusted for clinical variables, including LVEF and N-terminal pro-B-type natriuretic peptide (hazard ratio 2.48, 95% confidence interval 1.88-3.17 and 1.93, 1.48-2.52, respectively), while G-ID or ferritin <100 or <300 ng/ml were not. In HF outpatients, TSAT <20% is more consistently associated with congestion by ultrasound and poorer functional capacity than other ID definitions, irrespective of LVEF. TSAT <20% and iron ≤13 μmol/L, but not G-ID or ferritin-based ID, predict a worse prognosis in HF outpatients with preserved and reduced LVEF.

How much should the resting heart rate be controlled in patients with atrial fibrillation and coronary heart disease?

BackgroundCurrent guidelines recommend maintaining a resting heart rate below 100–110 beats per minute (bpm) for patients with atrial fibrillation (AF). However, there is a lack of research on various treatment approaches for patients with both atrial fibrillation and coronary heart disease (AF&CHD). We have discussed the primary therapeutic options for controlling resting heart rate in patients with AF&CHD.MethodsWe conducted a post hoc analysis of the AFFIRM (Atrial Fibrillation Follow-up Investigation of Rhythm Management) trial. Our study included 3986(98.2%) participants with AF, of which 1522 had AF&CHD. The primary outcome was all-cause mortality. We utilized the chi-squared test to identify significant differences in categorical variables. Multivariate Cox regression analysis was performed to compare the risk of death by standard deviation of heart rate visit to visit variability quartile.ResultsCompared to the AF group, a higher proportion of patients in the AF&CHD group had histories of smoking, hypertension, diabetes, heart failure, cardiomyopathy, valvular heart disease, peripheral vascular disease, bradycardia, hepatic or renal disease, pulmonary disease, and stroke (all P < 0.001). Additionally, patients with AF&CHD were more likely to undergo coronary artery bypass grafting (CABG) and pacemaker surgeries than those with AF alone. After adjusting for multiple clinical variables, patients with AF&CHD had a significantly higher risk of all-cause mortality compared to the AF group (RR = 1.79, 95% CI: 1.49–2.60; P < 0.01). Nonlinear multivariate curve-fitting analysis identified an optimal resting heart rate of 70 bpm for patients with AF&CHD (P < 0.001). Subgroup analysis revealed that patients with a resting heart rate below 70 bpm had lower all-cause mortality than those with a resting heart rate above 70 bpm (P < 0.001).ConclusionsFor resting heart rates below 70 bpm, our results suggest an inverse causal relationship between genetically determined resting heart rate and all-cause mortality in patients with AF&CHD.Clinical trial numberNot applicable.

Vitamin D and HIV: Description and correlation of serum levels with clinical and paraclinical variables

Introduction: Vitamin D (VD) has many extra-skeletal functions, such as immune regulation. It is important to recognize its role in patients infected by the human immunodeficiency virus (HIV). The objective of this study was to determine possible correlations between serum VD levels and multiple clinical and paraclinical variables in HIV-infected patients in the city of Bogotá, Colombia. Materials and methods: An observational, cross-sectional study was carried out on 161 HIV-infected patients in the city of Bogotá. VD levels were analyzed in relation to anthropometric classification of nutritional status, clinical and metabolic profile. Results: It was found a prevalence of low VD levels of 60.87%. Statistically significant correlations were detected between VD and the time of sun exposure reported by the patient and the type of antiretroviral therapy. In addition, using a linear regression model, it was found that the use of 2 nucleoside reverse transcriptase inhibitors + 1 integrase inhibitor correlates with hypovitaminosis, even with adequate time of sun exposure. No associations were found with age, sex, race, co morbidities, immune, or metabolic profile. Discussion: Although the pathophysiology of VD deficiency is multifactorial, low VD levels should be considered among the possible adverse effects of antiretro viral therapy and should be included in the usual follow-up of these patients.

Incremental prognostic value of cardiovascular magnetic resonance imaging in patients with severe LV dysfunction undergoing coronary artery bypass grafting.

Coronary artery disease (CAD) remains a significant global health issue, particularly when complicated by left ventricular ejection fraction (LVEF) < 35%. Although coronary artery bypass grafting (CABG) is recommended for such cases, the unclear prognosis necessitates further investigation. This retrospective study aimed to determine whether cardiovascular magnetic resonance (CMR) imaging provides additional prognostic value in guiding effective clinical management. The study included patients with CAD and LVEF < 35% who underwent CABG surgery after enhanced CMR between March 2016 and March 2023. CMR was performed using a 3.0T scanner with steady-state free precession and phase-sensitive inversion recovery sequences. Prognostic analysis of clinical and CMR data was conducted, with the endpoint defined as cardiovascular death, revascularization, hospitalization for heart failure, or stroke. Statistical analysis included Student's t-test, chi-squared test, univariate and multivariate Cox regression analysis, receiver operating characteristic analysis, Harrell C statistical analysis, integrated discrimination improvement (IDI), and net reclassification improvement (NRI) analysis. The study included 152 patients (mean age 58.6 ± 9.7 years; 138 men). During a mean follow-up of 2.0 years, 8 patients experienced cardiovascular death, while 1 case had revascularization, 13 had hospitalization for heart failure, and 11 had a stroke. Left atrial diameter index (LADi) (hazard ratio [HR], 1.08 [95% confidence interval (CI): 1.02-1.15]; P = 0.04) and late gadolinium enhancement (LGE) mass (HR, 1.03 [95% CI: 1.01-1.06]; P < 0.001) were associated with the endpoint, even after adjusting for multiple clinical variables. Adding LADi and LGE mass improved risk prediction for adverse events, as indicated by the C-index (0.738, p < 0.01), IDI (0.36), and NRI (0.13). Left atrial diameter index (LADi) and scar burden are valuable prognostic indicators in patients with LVEF < 35% undergoing CABG. They offer enhanced risk stratification beyond traditional clinical factors, highlighting their importance in guiding clinical management.

Clinical factors associated with day-to-day peak pain severity in individuals with chronic migraine: A cohort study using daily prospective diary data.

To describe the association between day-to-day peak pain severity and clinical factors in individuals with chronic migraine (CM). Little is known about how clinical factors relate to day-to-day pain severity in individuals with CM. Adults with CM were enrolled into this observational prospective cohort study that collected daily data about headache, associated symptoms, and lifestyle factors using a digital health platform (N1-Headache™) for 90 days. "Migraine days" were defined as days in which a headache occurred that had features described by the International Classification of Headache Disorders criteria. On these days, peak pain severity was recorded on a 4-point scale; on non-headache days peak pain severity was imputed as "0/none". The associations between peak pain severity and 12 clinical factors were modeled and adjusted for sex, age, daily headache, presence of menstrual bleeding, day of the week, and disability. All numerical and Likert scale variables were standardized prior to analysis. Data were available for 392 participants (35,280 tracked days). The sample was predominantly female (90.6%), with a mean (standard deviation) age of 39.9 (12.8) years. In the final multivariable model with random intercept and slopes, higher than typical self-reported levels of standardized stress (odds ratio [OR] 1.07, 95% confidence interval [CI] 1.04-1.11), standardized irritability (OR 1.05, 95% CI 1.02-1.08), standardized sadness (OR 1.05, 95% CI 1.02-1.07), fatigue (OR 1.25, 95% CI 1.15-1.36), eyestrain (OR 1.38, 95% CI 1.26-1.52), neck pain (OR 1.94, 95% CI 1.76-2.13), skin sensitivity (OR 1.61, 95% CI 1.44-1.80), and dehydration (OR 1.29, 95% CI 1.18-1.42) were associated with higher reported peak pain severity levels, while standardized sleep quality (OR 0.96, 95% CI 0.93-0.99) and standardized waking feeling refreshed (OR 0.84, 95% CI 0.81-0.88) were associated with lower reported peak pain severity levels. The inclusion of a random intercept and random slopes improved upon more parsimonious models and illustrated large differences in individuals' reporting of peak severity according to the levels of the associated clinical factors. Our data showed that the experience of CM, from a pain severity perspective, is complex, related to multiple clinical variables, and highly individualized. These results suggest that future work should aim to study a personalized approach to both medical and behavioral interventions for CM based on which clinical factors relate to the individual's experience of pain severity.

Machine learning-based predictive model for the development of thrombolysis resistance in patients with acute ischemic stroke

BackgroundThe objective of this study was to establish a predictive model utilizing machine learning techniques to anticipate the likelihood of thrombolysis resistance (TR) in acute ischaemic stroke (AIS) patients undergoing recombinant tissue plasminogen activator (rt-PA) intravenous thrombolysis, given that nearly half of such patients exhibit poor clinical outcomes.MethodsRetrospective clinical data were collected from AIS patients who underwent intravenous thrombolysis with rt-PA at the First Affiliated Hospital of Bengbu Medical University. Thrombolysis resistance was defined as ([National Institutes of Health Stroke Scale (NIHSS) at admission − 24-hour NIHSS] × 100％/ NIHSS at admission) ≤ 30%. In this study, we developed five machine learning models: logistic regression (LR), extreme gradient boosting (XGBoost), support vector machine (SVM), the least absolute shrinkage and selection operator (LASSO), and random forest (RF). We assessed the model’s performance by using receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis (DCA), and presented the results through a nomogram.ResultsThis study included a total of 218 patients with AIS who were treated with intravenous thrombolysis, 88 patients experienced TR. Among the five machine learning models, the LASSO model performed the best. The area under the curve (AUC) on the testing group was 0.765 (sensitivity: 0.767, specificity: 0.694, accuracy: 0.727). The apparent curve in the calibration curve was similar to the ideal curve, and DCA showed a positive net benefit. Key features associated with TR included NIHSS at admission, blood glucose, white blood cell count, neutrophil count, and blood urea nitrogen.ConclusionMachine learning methods with multiple clinical variables can help in early screening of patients at high risk of thrombolysis resistance, particularly in contexts where healthcare resources are limited.

Factors Influencing Discharges to Hospice for Patients With Late-Stage Huntington's Disease.

Background: Hospice services for patients with Huntington's disease (HD) are likely beneficial in relieving significant burdens and minimizing costly hospitalizations at the end of life, though there has been little study or clinical guidance on hospice enrollment for patients with HD. Objectives: The primary objective of this study was to identify clinical, sociodemographic, and system-level factors associated with discharges to hospice compared to other dispositions for hospitalized patients with late-stage HD. Methods: These analyses used data from the Nationwide Inpatient Sample between the years 2007 and 2011. Weighted logistic regression with a forward selection approach was performed to identify factors associated with discharge to hospice compared to discharge to home, facility, other locations, and death in hospital. Results: These analyses included 6544 hospitalizations of patients with late-stage HD. There was a significant increasing trend in discharges to hospice over the study period (P < 0.001). After adjustment, multiple clinical, sociodemographic, and system-level variables were identified as being associated with discharges to hospice. Patients with aspiration pneumonia and non-aspiration pneumonias had lower odds of being discharged to hospice compared to dying in the hospital. When comparing to discharges to facilities and home, weight loss and palliative care consultation were associated with greater odds of discharge to hospice. Conclusions: Our findings serve as a foundation for future studies on these factors, and thus help clinician decision-making on when to start advance care planning or end-of-life care for patients with HD. These results also support studies developing hospice referral criteria specific to patients with HD.

Machine Learning-Based Algorithm for the Early Prediction of Postoperative Hypocalcemia Risk After Thyroidectomy.

We used machine learning to develop and validate a multivariable algorithm allowing the accurate and early prediction of postoperative hypocalcemia risk. Postoperative hypocalcemia is frequent after total thyroidectomy. An early and accurate individualized prediction of the risk of hypocalcemia could guide the selective prescription of calcium supplementation only to patients most likely to present with hypocalcemia after total thyroidectomy. This retrospective study enrolled all patients undergoing total thyroidectomy in a single referral center between November 2019 and March 2022 (derivation cohort) and April 2022 and September 2022 (validation cohort). The primary study outcome was postoperative hypocalcemia (serum calcium under 80mg/L). Exposures were multiple clinical and biological variables prospectively collected and analyzed with various machine learning methods to develop and validate a multivariable prediction algorithm. Among 610/118 participants in the derivation/validation cohorts, 100 (16.4%)/26 (22%) presented postoperative hypocalcemia. The most accurate prediction algorithm was obtained with random forest and combined intraoperative parathyroid hormone measurements with 3 clinical variables (age, sex, and body mass index) to calculate a postoperative hypocalcemia risk for each patient. After multiple cross-validation, the area under the receiver operative characteristic curve was 0.902 (0.829-0.970) in the derivation cohort, and 0.928 (95% CI: 0.86; 0.97) in the validation cohort. Postoperative hypocalcemia risk values of 7% (low threshold) and 20% (high threshold) had, respectively, a sensitivity of 92%, a negative likelihood ratio of 0.11, a specificity of 90%, and a positive of 7.6 for the prediction of postoperative hypocalcemia. Using machine learning, we developed and validated a simple multivariable model that allowed the accurate prediction of postoperative hypocalcemia. The resulting algorithm could be used at the point of care to guide clinical management after total thyroidectomy.

Evaluation of Clinical Variables Affecting Myocardial Glucose Uptake in Cardiac FDG PET.

Cardiac 2-deoxy-2-[F-18]fluoro-D-glucose positron emission tomography (FDG PET) is widely used to assess myocardial viability in patients with ischemic heart disease. While sufficient glucose uptake is a prerequisite for accurate interpretation of cardiac viability, there are a lack of data on which clinical variables have the most significant impact on myocardial glucose metabolism. Therefore, this study was designed to evaluate several clinical variables that could affect myocardial glucose metabolism. A total of 214 consecutive cases were retrospectively enrolled in this study. All subjects received 250 mg of acipimox and underwent glucose loading as preparation for cardiac FDG PET/CT. Three-dimensional regions of interest (ROIs) were drawn on PET/CT fusion images. Myocardial glucose uptake ratio (MGUR = SUVmax of LV myocardium/SUVmean of liver) was then calculated. Multiple clinical variables including body mass index (BMI), blood glucose levels at different times, administered insulin dosage, lipid profiles, and ejection fraction were measured and analyzed for correlation with myocardial glucose uptake. After dichotomizing the subjects based on a BMI of 25, each group's MGUR was compared. Myocardial uptake showed significant correlations with BMI (r = -0.162, p = 0.018), HbA1c (r = -0.150, p = 0.030), and triglyceride levels (r = -0.137, p = 0.046). No other clinical variables showed a significant correlation with myocardial glucose uptake. After multiple linear regression analysis, BMI (p = 0.032) and HbA1c (p = 0.050) showed a correlation with MGUR. In group analysis, after dividing patients based on BMI, the obese group showed significantly lower myocardial uptake than the non-obese group (3.8 ± 1.9 vs. 4.4 ± 2.1, p = 0.031). Among several clinical variables, BMI and HbA1c levels were related to myocardial glucose uptake. A prospective study would be needed to examine whether a protocol that additionally considers BMI and HbA1c levels is necessary for the current cardiac FDG PET protocol.

The utility of multiple clinical variables in predicting survival outcome for patients with rectal cancer: An integrated regression and retrospective cohort analysis.

e15610 Background: Prior studies have investigated clinical factors impacting the survival rates of rectal cancer patients. However, a robust and precise model for discerning these risks, which can be identified by integrated regressions analysis has yet to be established. The aim of this study is to develop and evaluate the diagnostic value of the constructed prediction model based on selected clinical variables. Methods: Patients diagnosed with stage II-III rectal cancer (April 2015-June 2021) were retrospectively enrolled as the primary cohort. Individual variables were extracted and then subjected to a K-adaptive partitioning algorithm for cut-off point determination and grouping. The Cox and least absolute shrinkage and selection operator regression (LASSO) regression were applied for independent predictors selection over significant overall survival (OS), and a nomogram was subsequently constructed. The performance of the constructed model was evaluated using the receiver operating characteristics (ROC) curves, calibration curves, and decision curve analysis (DCA), and stepwise compared with data cohorts derived from TCGA and SEER databases. Results: The Cox-LASSO analysis results considered the four independent variables as significant predictors, yielding the risk score equation: 1.086(for CEA level≥7.5 ng/ml) -1.7756(if gender is male) +1.829*(pT stages) +0.582*(pN stages). Notably, the distal resection margin (DRM) was excluded from the final model following the analysis (p = 0.060, 95%CI: -0.04-2.17). The constructed model (C-index:0.790, 95%CI: 0.673-0.743, AUC:0.873) outperformed the conventional TNM staging models (C-index:0.539, 95%CI:0.121-0.957, AUC:0.735). It stratified patients into low- or high-risk groups with significant OS differences (93% vs. 37%, p≤0.0001, respectively). The calibration curves for OS probability showed strong homogeneities between predictions and actual observations. DCA revealed positive net benefits at evaluated first, third, and fifth years. Moreover, the external validations confirmed the accurate prediction following the constructed model, with AUC of 0.745 and 0.705 for the TCGA and SEER datasets, respectively. Conclusions: This study resulted in a novel and effective prediction model for rectal cancer patients based on conventional clinical characteristics. It demonstrated solid performance and can serve as a convenient tool for OS prediction, aiding in rectal cancer patients' risk scoring and clinical decision-making.

End of Life Outcomes Following Comfort Care Orders: A Single Center Experience.

Background: Few studies have explored the outcomes of patients placed on comfort care with respect to hospice disposition. The objective of this study was to perform a retrospective analysis of patients who transitioned to comfort care. Methods: We conducted a retrospective study of patients placed on the comfort care order set between July 1st, 2021, until June 30th, 2022. Each individual patient chart was then analyzed to collect multiple clinical variables. IRB approval was obtained as per institutional guidelines. Results: 541 patients were included in the analysis. An average of 1.5 patients were placed on comfort care a day. 424 (78.37%) patients died while in the hospital. The median time on comfort care was 1 day. For subspecialty and hospital medicine patients the median time was 2 days. 40% of non-ICU patients were discharged with hospice services. 60% of patients were in the intensive care unit (ICU) and spent a median of 2.33 hours on comfort care. 19% of these patients were on comfort care for over 12 hours. 94% of the patients placed on comfort care in the ICU died in the hospital as compared to 53% of subspecialty and 59% of hospital medicine patients. Conclusions: The majority of patients placed on comfort care died during their hospitalization demonstrating a real need for comprehensive end of life care and immediate hospice services. For those discharged with hospice services, they spent an excessive amount of time in the hospital waiting for services to be arranged.

Predictors of good functional outcome in ischaemic stroke patients without delayed neurological improvement after mechanical thrombectomy.

This study aimed to identify predictors of 90-day good functional outcome (GFO) in patients with acute ischaemic stroke (AIS) who were treated with mechanical thrombectomy but did not achieve a delayed neurological improvement (DNI). In-hospital neurological improvement in patients with AIS is consistently associated with long- -term GFO. Patients who experience neither early nor delayed neurological improvement can still achieve long-term GFO, but predictors of such an outcome have not been studied. This single-centre retrospective study involved 307 patients with anterior circulation AIS treated with mechanical thrombectomy. Multiple clinical, biochemical, radiological, and treatment-related variables were collected and analysed. DNI on day 7 was defined as at least a 10-point reduction in the National Institutes of Health Stroke Scale (NIHSS) score or NIHSS score < 2. GFO on day 90 was defined as a modified Rankin Scale (mRS) score ≤ 2. We compared the characteristics of patients with and without DNI, with special attention paid to patients who achieved 90-GFO despite a lack of DNI. Multivariate analyses were then performed to establish independent predictors of 90-day GFO among patients without DNI. DNI occurred in 150 out of 307 patients (48.7%) and significantly increased the odds for 90-day GFO (odds ratio [OR]: 13.99; p < 0.001). Among patients without DNI, 41.4% achieved 90-day GFO. Younger age (OR: 0.96; 95% confidence interval [CI]: 0.93-0.99; p = 0.008), lower baseline NIHSS score (OR: 0.80; 95% CI: 0.73-0.89; p < 0.001), treatment with intravenous thrombolysis (OR: 3.06; 95% CI: 1.25-7.49; p = 0.014), lack of an undetermined aetiology (OR: 0.40; 95% CI: 0.16-0.998; p = 0.050), lack of pneumonia (OR: 0.08; 95% CI: 0.02-0.31; p < 0.001), and higher haemoglobin concentration on admission (OR: 1.31; 95% CI: 1.04-1.69; p = 0.024) were identified as predictors of 90-day GFO in this subgroup. Almost half of patients with AIS in anterior circulation treated with mechanical thrombectomy experience DNI, which is a good predictor of 90-day GFO. Furthermore, 40% of patients without DNI achieve 90-day GFO which can be independently predicted by younger age, lower baseline NIHSS score, treatment with intravenous thrombolysis, higher haemoglobin concentration on admission, lack of undetermined ischaemic stroke aetiology, and lack of pneumonia.

Emergency physician gender and head computed tomography orders for older adults who have fallen.

Physicians vary in their computed tomography (CT) scan usage. It remains unclear how physician gender relates to clinical practice or patient outcomes. The aim of this study was to assess the association between physician gender and decision to order head CT scans for older emergency patients who had fallen. This was a secondary analysis of a prospective observational cohort study conducted in 11 hospital emergency departments (EDs) in Canada and the United States. The primary study enrolled patients who were 65 years and older who presented to the ED after a fall. The analysis evaluated treating physician gender adjusted for multiple clinical variables. Primary analysis used a hierarchical logistic regression model to evaluate the association between treating physician gender and the patient receiving a head CT scan. Secondary analysis reported the adjusted odds ratio (OR) for diagnosing intracranial bleeding by physician gender. There were 3663 patients and 256 physicians included in the primary analysis. In the adjusted analysis, women physicians were no more likely to order a head CT than men (OR 1.26, 95% confidence interval 0.98-1.61). In the secondary analysis of 2294 patients who received a head CT, physician gender was not associated with finding a clinically important intracranial bleed. There was no significant association between physician gender and ordering head CT scans for older emergency patients who had fallen. For patients where CT scans were ordered, there was no significant relationship between physician gender and the diagnosis of clinically important intracranial bleeding.

Multiparametric Aging Study Across Adulthood in the Leg Through Quantitative MR Imaging, 1H Spectroscopy, and 31P Spectroscopy at 3T.

Improved characterization of healthy muscle aging is needed to establish early biomarkers in age-related diseases. To quantify age-related changes on multiple MRI and clinical variables evaluated in the same cohort and identify correlations among them. Prospective. 70 healthy subjects (30 men) from 20 to 81 years old. 3T/water T2 (multiecho SE, multi-TE STEAM), water T1 (GRE MR Fingerprinting), fat-fraction (multiecho GRE, multi-TE STEAM), carnosine (PRESS), multicomponent water T2 (ISIS-CPMG SE train), and 31P pulse-acquire spectroscopy. Age- and sex-related changes on: Imaging: fat-fraction (FFMRI), water T1 (T1-H2O), and T2 (T2-H2O-MRI) and their heterogeneities ΔT1-H2O and ΔT2-H2O-MRI in the posterior compartment (PC) and anterior compartment (AC) of the leg. 1H spectroscopy: Carnosine concentration, pH, water T2 components (T2-H2O-CPMG), fat-fraction (FFMRS), and water T2 (T2-H2O-MRS) in the gastrocnemius medialis. 31P spectroscopy: Phosphodiesters (PDE), phosphomonoesters, inorganic phosphates (Pi), and phosphocreatine (PCr) normalized to adenosine triphosphate (ATP) and pH in the calf. Clinical evaluation: Body-mass index (BMI), gait speed (GS), plantar flexion strength, handgrip strength (HS), HS normalized to wrist circumference (HSnorm), physical activity assessment. Multilinear regressions with sex and age as fixed factors. Spearman correlations calculated between variables. Benjamini-Hochberg procedure for false positives reduction (5% rate). A P < 0.05 significance level was used. Significant age-related increases were found for BMI (ρAge = 0.04), HSnorm (ρAge = -0.01), PDE/ATP (ρAge = 2.8 × 10-3), Pi/ATP (ρAge = 2.0 × 10-3), Pi/PCr (ρAge = 0.3 × 10-3), T2-H2O-MRS (ρAge = 0.051 msec), FFMRS (ρAge = 0.036) the intermediate T2-H2O-CPMG component time (ρAge = 0.112 msec), and fraction (ρAge = -0.3 × 10-3); and in both compartments for FFMRI (ρAge = 0.06, PC; ρAge = 0.06, AC), T2-H2O-MRI (ρAge = 0.05, PC; ρAge = 0.05, AC; msec), ΔT2-H2O-MRI (ρAge = 0.02, PC; ρAge = 0.02, AC; msec), T1-H2O (ρAge = 1.08, PC; ρAge = 1.06, AC; msec), and ΔT1-H2O (ρAge = 0.22, PC; ρAge = 0.37, AC; msec). The best age predictors, accounting for sex-related differences, were HSnorm (R2 = 0.52) and PDE/ATP (R2 = 0.44). In both leg compartments, the imaging measures and HSnorm were intercorrelated. In PC, T2-H2O-MRS and FFMRS also showed numerous correlations to the imaging measures. PDE/ATP correlated to T1-H2O, T2-H2O-MRI, ΔT2-H2O-MRI, FFMRI, FFMRS, the intermediate T2-H2O-CPMG, BMI, Pi/PCr, and HSnorm. Our multiparametric MRI approach provided an integrative view of age-related changes in the leg and revealed multiple correlations between these parameters and the normalized HS. 1 TECHNICAL EFFICACY: Stage 3.

Creating synthetic populations in transplantation: A Bayesian approach enabling simulation without registry re-sampling.

Computer simulation has played a pivotal role in analyzing alternative organ allocation strategies in transplantation. The current approach to producing cohorts of organ donors and candidates for individual-level simulation requires directly re-sampling retrospective data from a transplant registry. This historical data may reflect outmoded policies and practices as well as systemic inequities in candidate listing, limiting contemporary applicability of simulation results. We describe the development of an alternative approach for generating synthetic donors and candidates using hierarchical Bayesian network probability models. We developed two Bayesian networks to model dependencies among 10 donor and 36 candidate characteristics relevant to waitlist survival, donor-candidate matching, and post-transplant survival. We estimated parameters for each model using Scientific Registry of Transplant Recipients (SRTR) data. For 100 donor and 100 candidate synthetic populations generated, proportions for each categorical donor or candidate attribute, respectively, fell within one percentage point of observed values; the interquartile ranges (IQRs) of each continuous variable contained the corresponding SRTR observed median. Comparisons of synthetic to observed stratified distributions demonstrated the ability of the method to capture complex joint variability among multiple characteristics. We also demonstrated how changing two upstream population parameters can exert cascading effects on multiple relevant clinical variables in a synthetic population. Generating synthetic donor and candidate populations in transplant simulation may help overcome critical limitations related to the re-sampling of historical data, allowing developers and decision makers to customize the parameters of these populations to reflect realistic or hypothetical future states.

Postpartum Haemorrhage Risk Prediction Model Developed by Machine Learning Algorithms: A Single-Centre Retrospective Analysis of Clinical Data

Background: Postpartum haemorrhage (PPH) is a serious complication and a cause of maternal mortality after delivery. This study used machine learning algorithms and new feature selection methods to build an efficient PPH risk prediction model and provided new ideas and reference methods for PPH risk management. Methods: The clinical data of women who gave birth at Wenzhou People’s Hospital from 1 January 2021, to 30 March 2022, were retrospectively analysed, and the women were divided into a high haemorrhage group (337 patients) and a low haemorrhage group (431 patients) based on the amount of blood loss. Machine learning algorithms were used to identify the features associated with postpartum haemorrhage from multiple clinical variables using feature selection methods, such as recursive feature elimination (RFE), recursive feature elimination with cross-validation (RFECV), and SelectKBest, and to establish prediction models. Results: For all women, the features associated with postpartum haemorrhage were ‘age’, ‘newborn weight’, ‘gestational week’, ‘perineal laceration’, and ‘caesarean section’. The prediction model established by the random forest classifier performed best, with an F1 score of 0.73 and an area under the curve (AUC) of 0.84. For women who underwent caesarean section or had a vaginal delivery, the features associated with postpartum haemorrhage risk were different. The risk factors for postpartum haemorrhage in women who underwent caesarean section were ‘age’, ‘parity’, ‘preterm birth’, and ‘placenta previa’. The prediction model established by the random forest classifier performed best, with an F1 value of 0.96 and an AUC of 0.95. The risk factors for postpartum haemorrhage in women with vaginal delivery were ‘age’, ‘parity’, ‘gestational week’, ‘diabetes’, ‘assisted reproduction’, ‘hypertension (preeclampsia)’, and ‘multiple pregnancy’. The prediction model established by the AdaBoost classifier performed best, with an F1 value of 0.65 and an AUC of 0.76. Conclusions: Machine learning algorithms can effectively identify the features associated with postpartum haemorrhage risk from clinical variables and establish accurate prediction models, offering a novel approach for clinicians to assess the risk of and prevent postpartum haemorrhage.

Long-term outcomes prediction in diabetic heart failure with preserved ejection fraction by cardiac MRI.

We aimed to explore imaging features including tissue characterization and myocardial deformation in diabetic heart failure with preserved ejection fraction (HFpEF)patients by magnetic resonance imaging (MRI) and investigate its prognostic value for adverse outcomes. Patients with HFpEF who underwent cardiac MRI between January 2010 and December 2016 were enrolled. Feature-tracking (FT) analysis and myocardial fibrosis were assessed by cardiac MRI. Cox proportional regression analysis was performed to determine the association between MRI variables and primary outcomes. Primary outcomes were all-cause death or heart failure hospitalization during the follow-up period. Of the 335 enrolled patients with HFpEF, 191 had diabetes mellitus (DM) (mean age: 58.7years ± 10.8; 137 men). During a median follow-up of 10.2years, 91 diabetic HFpEF and 56 non-diabetic HFpEF patients experienced primary outcomes. DM was a significant predictor of worse prognosis in HFpEF. In diabetic HFpEF, the addition of conventional imaging variables (left ventricular ejection fraction, left atrial volume index, extent of late gadolinium enhancement (LGE)) and global longitudinal strain (GLS) resulted in a significant increase in the area under the receiver operating characteristic curve (from 0.693 to 0.760, p < 0.05). After adjustment for multiple clinical and imaging variables, each 1% worsening in GLS was associated with a 9.8% increased risk of adverse events (p = 0.004). Diabetic HFpEF is characterized by more severely impaired strains and myocardial fibrosis, which is identified as a high-risk HFpEF phenotype. In diabetic HFpEF, comprehensive cardiac MRI provides incremental value in predicting prognosis. Particularly, MRI-FT measurement of GLS is an independent predictor of adverse outcome in diabetic HFpEF. Our findings suggested that MRI-derived variables, especially global longitudinal strain, played a crucial role in risk stratification and predicting worse prognosis in diabetic heart failure with preserved ejection fraction, which could assist in identifying high-risk patients and guiding therapeutic decision-making. • Limited data are available on the cardiac MRI features of diabetic heart failure with preserved ejection fraction, including myocardial deformation and tissue characterization, as well as their incremental prognostic value. • Diabetic heart failure with preserved ejection fraction patients was characterized by more impaired strains and myocardial fibrosis. Comprehensive MRI, including tissue characterization and global longitudinal strain, provided incremental value for risk prediction. • MRI served as a valuable tool for identifying high-risk patients and guiding clinical management in diabetic heart failure with preserved ejection fraction.