[Development and Validation of a Risk Prediction Model for Prolonged Hospitalization in Patients With Diabetic Foot Ulcers]. / ç³–å°¿ç— è¶³æºƒç–¡æ‚£è€ ä½é™¢æ—¶é—´å»¶é•¿é£Žé™©é¢„æµ‹æ¨¡åž‹æž„å»ºåŠéªŒè¯.

Objective: To investigate the risk factors associated with prolonged hospitalization in patients diagnosed with diabetic foot ulcers (DFU), to develop a predictive model, and to conduct internal validation of the model. Methods: The clinical data of DFU patients admitted to West China Hospital, Sichuan University between January 2012 and December 2022 were retrospectively collected. The subjects were randomly assigned to a training cohort and a validation cohort at a ratio of 7 to 3. Hospital stays longer than 75th percentile were defined as prolonged length-of-stay. A thorough analysis of the risk factors was conducted using the training cohort, which enabled the development of an accurate risk prediction model. To ensure robustness, the model was internally validated using the validation cohort. Results: A total of 967 inpatients with DFU were included, among whom 245 patients were identified as having an extended length-of-stay. The training cohort consisted of 622 patients, while the validation cohort comprised 291 patients. Multivariate logistic regression analysis revealed that smoking history (odds ratio [OR]=1.67, 95% confidence interval [CI], 1.13 to 2.48, P=0.010), Wagner grade 3 or higher (OR=7.13, 95% CI, 3.68 to 13.83, P<0.001), midfoot ulcers (OR=1.99, 95% CI, 1.07 to 3.72, P=0.030), posterior foot ulcers (OR=3.68, 95% CI, 1.83 to 7.41, P<0.001), multisite ulcers (OR=2.91, 95% CI, 1.80 to 4.69, P<0.001), wound size≥3 cm2 (OR=2.00, 95% CI, 1.28-3.11, P=0.002), and white blood cell count (OR=1.11, 95% CI, 1.05 to 1.18, P<0.001) were associated with an increased risk of prolonged length of stay. Additionally, a nomogram was constructed based on the identified risk factors. The areas under the receiver operating characteristic (ROC) curves for both the training cohort and the validation cohort were 0.782 (95% CI, 0.745 to 0.820) and 0.756 (95% CI, 0.694 to 0.818), respectively, indicating robust predictive performance. Furthermore, the calibration plot demonstrated optimal concordance between the predicted probabilities and the observed outcomes in both the training and the validation cohorts. Conclusion: Smoking history, Wagner grade≥3, midfoot ulcers, posterior foot ulcers, multisite ulcers, ulcer area≥3 cm2, and elevated white blood cell count are identified as independent predictors of prolonged hospitalization. Therefore, it is imperative that clinicians conduct a comprehensive patient evaluation and implement appropriate diagnostic and therapeutic strategies to effectively shorten the length of stay for DFU patients.

A novel nomogram for predicting prolonged mechanical ventilation after acute type A aortic dissection surgery: a retrospective study investigating the impact of ventilation duration on postoperative outcomes.

OBJECTIVE: Acute type A aortic dissection (ATAAD) is a devastating cardiovascular disease with extraordinary morbidity and mortality. Prolonged mechanical ventilation (PMV) is a common complication following ATAAD surgery, leading to adverse outcomes. This study aimed to investigate the correlation between mechanical ventilation time (MVT) and prognosis and to devise a nomogram for predicting PMV after ATAAD surgery. METHODS: This retrospective study enrolled 1049 ATAAD patients from 2011 to 2019. Subgroups were divided into < 12 h, 12 h to < 24 h, 24 h to < 48 h, 48 h to < 72 h, and ≥ 72 h according to MVT. Clinical characteristics and outcomes were compared among the groups. Using multivariable logistic regression analyses, we investigated the relationship between each stratification of MVT and mortality. A nomogram was constructed based on the refined multivariable logistic regression model for predicting PMV. RESULTS: The total mortality was 11.8% (124/1049). The results showed that the groups with MVT 48 h to < 72 h and ≥ 72 h had significantly higher operative mortality compared to other MVT categories. Multivariate logistic regression analysis showed that MVT ≥72 h was significantly associated with higher short-term mortality. Thus, a nomogram was presented to elucidate the association between PMV (MVT ≥72 h) and risk factors including advanced age, preoperative cerebral ischemia, ascending aorta replacement, concomitant coronary artery bypass grafting (CABG), longer cardiopulmonary bypass (CPB), and large-volume intraoperative fresh frozen plasma (FFP) transfusion. The nomogram exhibited strong predictive performance upon validation. CONCLUSIONS: Safely extubating patients within 72 h after ATAAD surgery is crucial for achieving favorable outcomes. The developed and validated nomogram provides a valuable tool for predicting PMV and optimizing postoperative care to improve patient prognosis. This novel nomogram has the potential to guide clinical decision-making and resource allocation in the management of ATAAD patients.

Prolonged mechanical ventilation (PMV) is a common complication following ATAAD surgery, leading to adverse outcomes.Safely extubating patients within 72 hours after ATAAD surgery is crucial for achieving favourable outcomes.A novel, validated nomogram incorporating risk factors such as age, comorbidities and intraoperative factors predicts PMV after ATAAD surgery, aiding clinical decision-making and optimizing postoperative care.

PredIL13: Stacking a variety of machine and deep learning methods with ESM-2 language model for identifying IL13-inducing peptides.

Interleukin (IL)-13 has emerged as one of the recently identified cytokine. Since IL-13 causes the severity of COVID-19 and alters crucial biological processes, it is urgent to explore novel molecules or peptides capable of including IL-13. Computational prediction has received attention as a complementary method to in-vivo and in-vitro experimental identification of IL-13 inducing peptides, because experimental identification is time-consuming, laborious, and expensive. A few computational tools have been presented, including the IL13Pred and iIL13Pred. To increase prediction capability, we have developed PredIL13, a cutting-edge ensemble learning method with the latest ESM-2 protein language model. This method stacked the probability scores outputted by 168 single-feature machine/deep learning models, and then trained a logistic regression-based meta-classifier with the stacked probability score vectors. The key technology was to implement ESM-2 and to select the optimal single-feature models according to their absolute weight coefficient for logistic regression (AWCLR), an indicator of the importance of each single-feature model. Especially, the sequential deletion of single-feature models based on the iterative AWCLR ranking (SDIWC) method constructed the meta-classifier consisting of the top 16 single-feature models, named PredIL13, while considering the model's accuracy. The PredIL13 greatly outperformed the-state-of-the-art predictors, thus is an invaluable tool for accelerating the detection of IL13-inducing peptide within the human genome.

An approach for developing a blood-based screening panel for lung cancer based on clonal hematopoietic mutations.

Early detection can significantly reduce mortality due to lung cancer. Presented here is an approach for developing a blood-based screening panel based on clonal hematopoietic mutations. Animal model studies suggest that clonal hematopoietic mutations in tumor infiltrating immune cells can modulate cancer progression, representing potential predictive biomarkers. The goal of this study was to determine if the clonal expansion of these mutations in blood samples could predict the occurrence of lung cancer. A set of 98 potentially pathogenic clonal hematopoietic mutations in tumor infiltrating immune cells were identified using sequencing data from lung cancer samples. These mutations were used as predictors to develop a logistic regression machine learning model. The model was tested on sequencing data from a separate set of 578 lung cancer and 545 non-cancer samples from 18 different cohorts. The logistic regression model correctly classified lung cancer and non-cancer blood samples with 94.12% sensitivity (95% Confidence Interval: 92.20-96.04%) and 85.96% specificity (95% Confidence Interval: 82.98-88.95%). Our results suggest that it may be possible to develop an accurate blood-based lung cancer screening panel using this approach. Unlike most other "liquid biopsies" currently under development, the approach presented here is based on standard sequencing protocols and uses a relatively small number of rationally selected mutations as predictors.

Clinical decision-making on lung cancer investigations in primary care: a vignette study.

OBJECTIVES: To investigate the role of comorbid chronic obstructive pulmonary disease (COPD) and symptom type on general practitioners' (GP's) symptom attribution and clinical decision-making in relation to lung cancer diagnosis. DESIGN: Vignette survey with a 2×2 mixed factorial design. SETTING: A nationwide online survey exploring clinical decision-making in primary care. PARTICIPANTS: 109 GPs based in the United Kingdom (UK) who were registered as responders on Dynata (an online survey platform). INTERVENTIONS: GPs were presented with four vignettes which described a patient aged 75 with a smoking history presenting with worsening symptoms (either general or respiratory) and with or without a pre-existing diagnosis of COPD. PRIMARY AND SECONDARY OUTCOME MEASURES: GPs indicated the three most likely diagnoses (free-text) and selected four management approaches (20 pre-coded options). Attribution of symptoms to lung cancer and referral for urgent chest X-ray were primary outcomes. Alternative diagnoses and management approaches were explored as secondary outcomes. Multivariable mixed-effects logistic regression was used, including random intercepts for individual GPs. RESULTS: 422 vignettes were completed. There was no evidence for COPD status as a predictor of lung cancer attribution (OR=1.1, 95% CI=0.5-2.4, p=0.914). There was no evidence for COPD status as a predictor of urgent chest X-ray referral (OR=0.6, 95% CI=0.3-1.2, p=0.12) or as a predictor when in combination with symptom type (OR=0.9, 95% CI=0.5-1.8, p=0.767). CONCLUSIONS: Lung cancer was identified as a possible diagnosis for persistent respiratory by only one out of five GPs, irrespective of the patients' COPD status. Increasing awareness among GPs of the link between COPD and lung cancer may increase the propensity for performing chest X-rays and referral for diagnostic testing for symptomatic patients.

The prediction model of fall risk for the elderly based on gait analysis.

INTRODUCTION: Early screening and identification are crucial for fall prevention, and developing a new method to predict fall risk in the elderly can address the current lack of objectivity in assessment tools. METHODS: A total of 132 elderly individuals over 80 years old residing in some nursing homes in Shanghai were selected using a convenient sampling method. Fall history information was collected, and gait data during a 10-meter walk were recorded. Logistic regression was employed to establish the prediction model, and a nomogram was used to assess the importance of the indicators. The Bootstrap method was utilized for internal validation of the model, while the verification set was used for external validation. The predictive performance of the model was evaluated using the area under the ROC curve, calibration curve, and decision curve analysis (DCA) to assess clinical benefits. RESULTS: The incidence of falls in the sample population was 36.4%. The Tinetti Gait and Balance Test (TGBT) score (OR = 0.832, 95% CI: 0.734,0.944), stride length (OR = 0.007, 95% CI: 0.000,0.104), difference in standing time (OR = 0.001, 95% CI: 0.000,0.742), and mean stride time (OR = 0.992, 95% CI:0.984,1.000) were identified as significant factors. The area under the ROC curve was 0.878 (95% CI: 0.805, 0.952), with a sensitivity of 0.935 and specificity of 0.726. The Brier score was 0.135, and the Hosmer-Lemeshow test (χ2 = 10.650, P = 0.222) indicated a good fit and calibration of the model. CONCLUSION: The TGBT score, stride length, difference in standing time, and stride time are all protective factors associated with fall risk among the elderly. The developed risk prediction model demonstrates good discrimination and calibration, providing valuable insights for early screening and intervention in fall risk among older adults.

Adherence patterns 1 year after initiation of SGLT2 inhibitors: results of a national cohort study.

OBJECTIVES: Adherence to medications is important for the management of chronic diseases. Although the proportion of days covered (PDC) is a common metric for measuring adherence, it may be insufficient to distinguish relevant differences in medication-taking behavior. Group-based trajectory models (GBTMs) have been used to better represent adherence over time. This study aims to examine adherence patterns 1 year after initiation among users of sodium-glucose cotransporter 2 (SGLT2) inhibitors using GBTMs and evaluate the ability of baseline characteristics to predict adherence trajectory. STUDY DESIGN: SGLT2 inhibitor new-user cohort study from 2014 to 2018. METHODS: We calculated 12-month PDC and categorized patients with PDC of 80% or greater as adherent. We performed multivariable logistic regression on adherence status controlling for baseline covariates. GBTMs were fit to identify adherence patterns 12 months following SGLT2 inhibitor initiation. Five multinomial logistic regression models including different subsets of predictors were used to predict adherence trajectory group assignment. RESULTS: In a cohort of 228,363 SGLT2 inhibitor users, the mean PDC was 57%, with 36% of the cohort being adherent. Overall, women and patients with anxiety or depression were less likely to be adherent. Six patterns of SGLT2 inhibitor adherence were identified with GBTMs: 1 fill (PDC = 0.08), early discontinuation (PDC = 0.22), consistently low adherence (PDC = 0.35), moderate adherence (PDC = 0.48), high adherence (PDC = 0.79), and near-perfect adherence (PDC = 0.95). All prediction models showed poor predictive accuracy (0.35). CONCLUSIONS: We found wide variation in adherence patterns among SGLT2 inhibitor users in a national cohort. Predictors from a health care claims database were unable to accurately predict adherence trajectory.

A machine learning technology for addressing medication-related risk in older, multimorbid patients.

OBJECTIVES: To evaluate the FeelBetter machine learning system's ability to accurately identify older patients with multimorbidity at Brigham and Women's Hospital at highest risk of medication-associated emergency department (ED) visits and hospitalizations, and to assess the system's ability to provide accurate medication recommendations for these patients. STUDY DESIGN: Retrospective cohort study. METHODS: The system uses medications, demographics, diagnoses, laboratory results, health care utilization patterns, and costs to stratify patients' risk of ED visits and hospitalizations. Patients were assigned 1 of 22 risk levels based on their system-generated risk percentile of either ED visits or hospitalizations. Logistic regression models were used to estimate the odds of ED visits and hospitalizations associated with each successive risk level compared with the 45th to 50th percentiles. After stratification, 100 high-risk (95th-100th percentiles) and 100 medium-risk (45th-55th percentiles) patients were randomly selected for generation of medication recommendations. Two clinical pharmacists reviewed the system-generated medication recommendations for these patients. RESULTS: Logistic regression models predicting 3-month utilization showed that compared with the 45th to 50th percentiles, patients in the top 1% risk percentile had ORs of 7.9 and 17.3 for ED visits and hospitalizations, respectively. The first 5 high-priority medications on each patient's medication list were associated with a mean (SD) of 6.65 (4.09) warnings. Of 1290 warnings reviewed, 1151 (89.2%) were assessed as correct. CONCLUSIONS: The FeelBetter system effectively stratifies older patients with multimorbidity at risk of ED use and hospitalizations. Medication recommendations provided by the system are largely accurate and can potentially be beneficial for patient care.

A predictive score for atrial fibrillation in poststroke patients.

BACKGROUND: Atrial fibrillation (AF) is a risk factor for cerebral ischemia. Identifying the presence of AF, especially in paroxysmal cases, may take time and lacks clear support in the literature regarding the optimal investigative approach; in resource-limited settings, identifying a higher-risk group for AF can assist in planning further investigation. OBJECTIVE: To develop a scoring tool to predict the risk of incident AF in the poststroke follow-up. METHODS: A retrospective longitudinal study with data collected from electronic medical records of patients hospitalized and followed up for cerebral ischemia from 2014 to 2021 at a tertiary stroke center. Demographic, clinical, laboratory, electrocardiogram, and echocardiogram data, as well as neuroimaging data, were collected. Stepwise logistic regression was employed to identify associated variables. A score with integer numbers was created based on beta coefficients. Calibration and validation were performed to evaluate accuracy. RESULTS: We included 872 patients in the final analysis. The score was created with left atrial diameter ≥ 42 mm (2 points), age ≥ 70 years (1 point), presence of septal aneurysm (2 points), and score ≥ 6 points at admission on the National Institutes of Health Stroke Scale (NIHSS; 1 point). The score ranges from 0 to 6. Patients with a score ≥ 2 points had a fivefold increased risk of having AF detected in the follow-up. The area under the curve (AUC) was of 0.77 (0.72-0.85). CONCLUSION: We were able structure an accurate risk score tool for incident AF, which could be validated in multicenter samples in future studies.

ANTECEDENTES: Fibrilação atrial (FA) é um fator de risco para isquemia cerebral. Identificar a presença de FA, especialmente em casos paroxísticos, pode demandar tempo, e não há fundamentos claros na literatura quanto ao melhor método de proceder à investigação; em locais de parcos recursos, identificar um grupo de mais alto risco de FA pode auxiliar no planejamento da investigação complementar. OBJETIVO: Desenvolver uma ferramenta de escore para prever o risco de FA no acompanhamento após acidente vascular cerebral (AVC). MéTODOS: Estudo longitudinal retrospectivo, com dados coletados dos prontuários eletrônicos de pacientes hospitalizados e acompanhados ambulatorialmente por isquemia cerebral, de 2014 a 2021, em um centro de AVC terciário. Foram coleados dados demográficos, clínicos, laboratoriais, de eletrocardiograma e ecocardiograma, além de dados de neuroimagem. Mediante uma regressão logística por stepwise, foram identificadas variáveis associadas. Um escore com números inteiros foi criado com base nos coeficientes beta. Calibração e validação foram realizadas para avaliar a precisão. RESULTADOS: Foram incluídos 872 pacientes na análise final. O escore foi criado com diâmetro de átrio esquerdo ≥ 42 mm (2 pontos), idade ≥ 70 anos (1 ponto), presença de aneurisma septal (2 pontos) e pontuação à admissão ≥ 6 na escala de AVC dos National Institutes of Health (National Institutes of Health Stroke Scale, NIHSS, em inglês; 1 ponto). O escore tem pontuação que varia de 0 a 6. Pacientes com escore ≥ 2 pontos tiveram cinco vezes mais risco de terem FA detectada no acompanhamento. A área sob a curva (area under curve, AUC, em inglês) foi de 0.77 (0.72­0.85). CONCLUSãO: Pudemos estruturar uma ferramenta precisa de escore de risco de FA, a qual poderá ser validada em amostras multicêntricas em estudos futuros.

Application of multivariate binary logistic regression grouped outlier statistics and geospatial logistic model to identify villages having unusual health-seeking habits for childhood malaria in Malawi.

BACKGROUND: Early diagnosis and prompt treatment of malaria in young children are crucial for preventing the serious stages of the disease. If delayed treatment-seeking habits are observed in certain areas, targeted campaigns and interventions can be implemented to improve the situation. METHODS: This study applied multivariate binary logistic regression model diagnostics and geospatial logistic model to identify traditional authorities in Malawi where caregivers have unusual health-seeking behaviour for childhood malaria. The data from the 2021 Malawi Malaria Indicator Survey were analysed using R software version 4.3.0 for regressions and STATA version 17 for data cleaning. RESULTS: Both models showed significant variability in treatment-seeking habits of caregivers between villages. The mixed-effects logit model residual identified Vuso Jere, Kampingo Sibande, Ngabu, and Dzoole as outliers in the model. Despite characteristics that promote late reporting of malaria at clinics, most mothers in these traditional authorities sought treatment within twenty-four hours of the onset of malaria symptoms in their children. On the other hand, the geospatial logit model showed that late seeking of malaria treatment was prevalent in most areas of the country, except a few traditional authorities such as Mwakaboko, Mwenemisuku, Mwabulambya, Mmbelwa, Mwadzama, Zulu, Amidu, Kasisi, and Mabuka. CONCLUSIONS: These findings suggest that using a combination of multivariate regression model residuals and geospatial statistics can help in identifying communities with distinct treatment-seeking patterns for childhood malaria within a population. Health policymakers could benefit from consulting traditional authorities who demonstrated early reporting for care in this study. This could help in understanding the best practices followed by mothers in those areas which can be replicated in regions where seeking care is delayed.

Construction and validation of a risk prediction model for early hungry bone syndrome in maintenance hemodialysis patients post-parathyroidectomy. / ç»´æŒæ€§è¡€æ¶²é€æžæ‚£è€ ç”²çŠ¶æ—è ºåˆ‡é™¤æœ¯åŽæ—©æœŸéª¨é¥¥é¥¿ç»¼åˆå¾é£Žé™©é¢„æµ‹æ¨¡åž‹çš„æž„å»ºä¸ŽéªŒè¯.

OBJECTIVES: Parathyroidectomy (PTX) is an effective treatment for refractory secondary hyperparathyroidism (SHPT), but it can lead to hungry bone syndrome (HBS), significantly threatening the health of maintenance haemodialysis (MHD) patients. While previous studies have analyzed the risk factors for HBS post-PTX, the predictive performance and clinical applicability of these risk models need further validation. This study aims to construct and validate a risk prediction model for HBS in MHD patients with SHPT post-PTX. METHODS: A retrospective analysis was conducted on 368 MHD patients with SHPT who underwent PTX at Changsha Jieao Nephrology Hospital from January 2020 to December 2021. Patients were divided into a HBS group and a non-HBS group based on the occurrence of HBS. General data, surgical information, and biochemical indicators were compared between the 2 groups. Multivariate logistic regression was used to identify factors influencing HBS, and a risk prediction model was established. The model's performance was evaluated using receiver operator characteristic (ROC) curves, decision curves, and calibration curves. External validation was performed on 170 MHD patients with SHPT who underwent PTX at the Third Xiangya Hospital of Central South University from January to December 2022. RESULTS: The incidence of HBS post-PTX in MHD patients with SHPT was 60.60%. Logistic regression analysis identified preoperative bone involvement (OR=3.908, 95% CI 2.179 to 7.171), preoperative serum calcium (OR=7.174, 95% CI 2.291 to 24.015), preoperative intact parathyroid hormone (iPTH) (OR=1.001, 95% CI 1.001 to 1.001), preoperative alkaline phosphatase (ALP) (OR=1.001, 95% CI 1.000 to 1.001), and serum calcium on the first postoperative day (OR=0.006, 95% CI 0.001 to 0.038) as independent risk factors for HBS (all P<0.01). The constructed risk prediction model demonstrated good predictive performance in both internal and external validation cohorts. The internal validation cohort showed an accuracy of 0.821, sensitivity of 0.890, specificity of 0.776, Youden index of 0.666, and area under the curve (AUC) of 0.882 (95% CI 0.845 to 0.919). The external validation cohort showed an accuracy of 0.800, sensitivity of 0.806, specificity of 0.799, Youden index of 0.605, and AUC of 0.863 (95% CI 0.795 to 0.932). CONCLUSIONS: Preoperative bone involvement, serum calcium, iPTH, ALP, and serum calcium on the first postoperative day are influencing factors for HBS in MHD patients with SHPT post-PTX. The constructed risk prediction model based on these factors is reliable.

Assessing maternity care access: impacts on cesarean sections and dystocia.

BACKGROUND: As South Korea grapples with a declining birthrate, maternity care accessibility has become challenging. This study examines the association with labour intervention and pregnancy complication, specifically focusing on C-section and dystocia in maternity disparities. METHODS: Data from the South Korean NHIS-NID was used to analyze 1,437,186 women with childbirths between 2010 and 2015. The research defines 50 specific districts as Obstetrically Underserved Areas produced by the Ministry of Health and Welfare in 2011. C-Section were assessed through using medical procedure and DRG codes, while dystocia was defined using ICD-10 code. Logistic regression analysis was used to examine the significance of the association. RESULTS: Among the population residing in underserved areas, 42,873 out of a total of 1,437,186 individuals were identified. For nationwide cases, the odds ratios (ORs) for C-Section were 1.11 (95% CI: 1.08-1.13) and dystocia were 1.07 (95% CI: 1.05-1.09). In relatively accessible urban areas, the ORs for C-Section and dystocia, based on whether they were obstetrically underserved areas, were 1.16 (95% CI: 1.13-1.18) and 1.10 (95% CI: 1.08-1.19), respectively. CONCLUSION: Poor accessibility to maternity care facilities is closely linked to high-risk pregnancies, including an increased incidence of dystocia and a higher rate of C-sections. Insufficient access to maternity care not only raises the risk of serious pregnancy complications. Consequently, there is a pressing need for multi-faceted efforts to bridge this disparity.

Development and validation of a nomogram based on Lasso-Logistic regression for predicting splenomegaly secondary to acute pancreatitis.

PURPOSE: Investigate the clinical characteristics of splenomegaly secondary to acute pancreatitis (SSAP) and construct a nomogram prediction model based on Lasso-Logistic regression. METHODS: A retrospective case-control study was conducted to analyze the laboratory parameters and computed tomography (CT) imaging of acute pancreatitis (AP) patients recruited at Xuanwu Hospital from December 2014 to December 2021. Lasso regression was used to identify risk factors, and a novel nomogram was developed. The performance of the nomogram in discrimination, calibration, and clinical usefulness was evaluated through internal validation. RESULTS: The prevalence of SSAP was 9.2% (88/950), with the first detection occurring 65(30, 125) days after AP onset. Compared with the control group, the SSAP group exhibited a higher frequency of persistent respiratory failure, persistent renal failure, infected pancreatic necrosis, and severe AP, along with an increased need for surgery and longer hospital stay (P < 0.05 for all). There were 185 and 79 patients in the training and internal validation cohorts, respectively. Variables screened by Lasso regression, including platelet count, white blood cell (WBC) count, local complications, and modified CT severity index (mCTSI), were incorporated into the Logistic model. Multivariate analysis showed that WBC count ≦9.71 × 109/L, platelet count ≦140 × 109/L, mCTSI ≧8, and the presence of local complications were independently associated with the occurrence of SSAP. The area under the receiver operating characteristic curve was 0.790. The Hosmer-Lemeshow test showed that the model had good fitness (P = 0.954). Additionally, the nomogram performed well in the internal validation cohorts. CONCLUSIONS: SSAP is relatively common, and patients with this condition often have a worse clinical prognosis. Patients with low WBC and platelet counts, high mCTSI, and local complications in the early stages of the illness are at a higher risk for SSAP. A simple nomogram tool can be helpful for early prediction of SSAP.

Association between dietary niacin intake and benign prostatic hyperplasia: a population-based results from NHANES 2003-2008.

PURPOSE: Benign prostatic hyperplasia (BPH) commonly impacts the quality of life in older men. However, there is lack of research on relationship between dietary niacin intake and the risk of BPH. The purpose of this study was to investigate the relationship between dietary niacin intake and the risk of BPH. METHODS: Data from the NHANES spanning 2003 to 2008 were utilized. BPH was determined using a self-report questionnaire, while dietary niacin intake was calculated based on the mean of two distinct diet interviews. Multivariate logistic regressions were performed to explore the association, supplemented with restricted cubic splines and subgroup analysis. RESULTS: A total of 700 males were enrolled, of which 653 men had BPH. After adjusting for all covariates, a high dietary intake of niacin was associated with an increased risk of BPH (OR: 1.04; 95%CI: 1.01-1.07). Furthermore, when the lowest dietary niacin intake is used as the reference, the highest tertile is associated with an increased risk of BPH (OR: 2.34, 95% CI: 1.24-4,42). Restricted cubic splines demonstrated a positive correlation between dietary niacin intake and BPH risk. CONCLUSIONS: The study results demonstrated a positive association between dietary niacin intake and the risk of BPH in elderly men in the US. These findings underscore the importance of systematic assessment before supplementing micronutrients in elderly men.

Relapses in canine leishmaniosis: risk factors identified through mixed-effects logistic regression.

BACKGROUND: Canine leishmaniosis (CanL), caused by Leishmania infantum, is an important vector-borne parasitic disease in dogs with implications for human health. Despite advancements, managing CanL remains challenging due to its complexity, especially in chronic, relapsing cases. Mathematical modeling has emerged as a powerful tool in various medical fields, but its application in understanding CanL relapses remains unexplored. METHODS: This retrospective study aimed to investigate risk factors associated with disease relapse in a cohort of dogs naturally infected with L. infantum. Data from 291 repeated measures of 54 dogs meeting the inclusion criteria were included. Two logistic mixed-effects models were created to identify clinicopathological variables associated with an increased risk of clinical relapses requiring a leishmanicidal treatment in CanL. A backward elimination approach was employed, starting with a full model comprising all potential predictors. Variables were iteratively eliminated on the basis of their impact on the model, considering both statistical significance and model complexity. All analyses were conducted using R software, primarily employing the lme4 package, and applying a significance level of 5% (P < 0.05). RESULTS: This study identified clinicopathological variables associated with an increased risk of relapses requiring a leishmanicidal treatment. Model 1 revealed that for each 0.1 increase in the albumin/globulin ratio (A/G) ratio, the odds of requiring treatment decreased by 45%. Conversely, for each unit increase in the total clinical score (CS), the odds of requiring treatment increase by 22-30%. Indirect immunofluorescence antibody test (IFAT) was not a significant risk factor in model 1. Model 2, incorporating individual albumin and globulins values, showed that dogs with high IFAT titers, hyper beta-globulinemia, hypoalbuminemia, anemia, and high CS were at increased risk of relapse. Both models demonstrated a good fit and explained a substantial amount of variability in treatment decisions. CONCLUSIONS: Dogs exhibiting higher CS, dysproteinemia, anemia, and high IFAT titers are at increased risk of requiring leishmanicidal treatment upon clinical relapse in CanL. Regular monitoring and assessment of risk factors prove essential for early detection of relapses and effective intervention in CanL cases. The contrasting findings between the two models highlight the complexity of aspects influencing treatment decisions in this disease and the importance of tailored management strategies to improve outcomes for affected dogs.

Association of non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) and gallstones among US adults aged ≤ 50 years: a cross-sectional study from NHANES 2017-2020.

BACKGROUND: The chronic digestive condition gallstones is quite common around the world, the development of which is closely related to oxidative stress, inflammatory response and abnormalities of lipid metabolism. In the last few years, as a novel biomarker of lipid metabolism, the non-high-density lipoprotein cholesterol to high-density lipoprotein cholesterol ratio (NHHR) has garnered significant interest. However, its relationship with gallstones has not been studied yet. METHODS: 3,772 people, all under 50, were included in this study, and their full data came from the National Health and Nutrition Examination Survey (NHANES) database for the years 2017-2020. Information on gallstones was obtained through self-reported questionnaires. Smoothed curve fitting multifactorial logistic regression was utilized to evaluate the connection of NHHR with gallstone formation incidence. Subsequently, subgroup analysis and interaction tests were applied. Finally, to create a prediction model, logistic regression and feature screening by last absolute shrinkage and selection operator (LASSO) were used. The resulting model was displayed using a nomogram. RESULTS: In multivariate logistic regression that accounted for all factors, there was a 77% increase in the likelihood of gallstones for every unit rise in lnNHHR (OR 1.77 [CI 1.11-2.83]). Following NHHR stratification, the Q4 NHHR level was substantially more linked to the risk of gallstones than the Q1 level (OR 1.86 [CI 1.04-3.32]). This correlation was stronger in women, people under 35, smokers, abstainers from alcohol, non-Hispanic White people, those with excessively high cholesterol, people with COPD, and people without diabetes. After feature screening, a predictive model and visualized nomogram for gallstones were constructed with an AUC of 0.785 (CI 0.745-0.819), which was assessed by DCA to be clinically important. CONCLUSION: In the group of people ≤ 50 years of age, elevated NHHR levels were substantially linked to a higher incidence of gallstones. This correlation was stronger in several specific groups such as females, under 35 years of age, smokers, and so on. Predictive models constructed using the NHHR have potential clinical value in assessing gallstone formation.

The inverse associations of glycine and histidine in diet with hyperlipidemia and hypertension.

BACKGROUND: Amino acids are crucial for nutrition and metabolism, regulating metabolic pathways and activities vital to organismal health and stability. Glycine and histidine act as potent antioxidants and anti-inflammatory agents; however, limited knowledge exists regarding the associations between these amino acids and hyperlipidemia and hypertension. The purpose of this study is to investigate the relationship between dietary glycine and histidine, and hyperlipidemia and hypertension. METHODS: This population-based cross-sectional study evaluated the influence of dietary glycine and histidine, as well as their combined effect, on hyperlipidemia and hypertension in Chinese adults participating in the Nutrition Health Atlas Project (NHAP). General characteristics were acquired using a verified Internet-based Dietary Questionnaire for the Chinese. Binary logistic regression, along with gender, age groups, and median energy intake subgroup analyses, was employed to investigate the associations between dietary glycine and histidine and hyperlipidemia and hypertension. A sensitivity analysis was conducted to assess the impact of excluding individuals who smoke and consume alcohol on the results. RESULTS: Based on the study's findings, 418 out of 1091 cases had hyperlipidemia, whereas 673 had hypertension. A significant inverse relationship was found between dietary glycine, histidine, and glycine + histidine and hyperlipidemia and hypertension. Compared with the 1st and 2nd tertiles, the multivariable-adjusted odd ratios (ORs) (95% confidence intervals) (CIs) of the 3rd tertile of dietary glycine for hyperlipidemia and hypertension were 0.64 (0.49-0.84) (p < 0.01) and 0.70 (0.56-0.88) (p < 0.001); histidine was 0.63 (0.49-0.82) (p < 0.01) and 0.80 (0.64-0.99) (p < 0.01); and glycine + histidine was 0.64 (0.49-0.83) (p < 0.01) and 0.74 (0.59-0.92) (p < 0.001), respectively. High glycine and high histidine (HGHH) intake were negatively associated with hyperlipidemia and hypertension OR (95% CIs) were: 0.71 (0.58-0.88) (p < 0.01) and 0.73 (0.61-0.87) (p < 0.01), respectively. CONCLUSIONS: Dietary glycine and histidine, as well as their HGHH group, revealed an inverse relationship with hyperlipidemia and hypertension. Further investigations are needed to validate these findings.

Multiple Logistic Regression Model for Assessing the Risk Factors of Traffic Accidents: Khon Kaen Model.

BACKGROUND: Thailand has consistently held the highest global ranking in traffic accidents since 2017, with Khon Kaen displaying the highest mortality rate in the Department of Disease Control Region 7. OBJECTIVES: This study aims to utilize Injury Surveillance (IS) data to identify risk factors associated with emergency room (ER) outcomes at the Emergency Department of Khon Kaen hospital in Khon Kaen Municipality. METHODS: Data from the Injury Surveillance system's (IS system) database were collected, focusing on severity outcomes, time of events, and risk behaviors from January 1, 2008, to December 31, 2021. Data analysis was conducted using the R program, employing the Chi-square or independent T test to compare results and analyze associations between potential risk factors and ER outcomes. Multiple logistic regression (MLR) was used for classification analysis, and a confusion matrix was applied to evaluate the performance of the models. RESULTS: MLR analysis revealed that being male, age, alcohol consumption, and nighttime driving were more likely to increase the probability of severity outcomes. CONCLUSION: Being male, age, alcohol consumption, and nighttime driving are identified as potential risk factors contributing to the development of severity outcomes following traffic accidents.

Social support mediates social frailty with anxiety and depression.

BACKGROUND: Anxiety and depression are prevalent comorbidities in patients with chronic obstructive pulmonary disease (COPD). However, existing research has yielded conflicting findings regarding the effects of social frailty on anxiety and depression. The primary aim of this study is to validate the relationship between social frailty and social support with anxiety and depression in patients with acute exacerbations of COPD (AECOPD) and to investigate whether social support could explain the variations in prior study outcomes for patients with AECOPD. METHODS: Of the 315 patients hospitalized with AECOPD at the respiratory intensive care unit of a large tertiary care institution in Sichuan Province of China, between August 2022 and June 2023 who were surveyed, 306 were included in the analysis after excluding missing data. We conducted a logistic regression analysis to examine the associations of social frailty and social support with anxiety and depression and performed mediation analyses to examine whether social support mediates the relationship of social frailty with anxiety and depression. RESULTS: The logistic regression analysis revealed that social frailty did not associate anxiety or depression in patients with AECOPD. The mediation analysis supported this idea and indicated that while social frailty does not directly influence anxiety or depression, it can through social support. CONCLUSIONS: The findings suggest that while social frailty may not directly impact anxiety or depression in patients with AECOPD, social support plays a crucial mediating role. Enhancing social support can indirectly alleviate anxiety and depression among these patients. Enhancing social support networks should thus be prioritized by healthcare providers and family members to improve mental health outcomes in this patient population.

Machine Learning and Clinical Predictors of Mortality in Cardiac Arrest Patients: A Comprehensive Analysis.

BACKGROUND Cardiac arrest (CA) is a global public health challenge. This study explored the predictors of mortality and their interactions utilizing machine learning algorithms and their related mortality odds among patients following CA. MATERIAL AND METHODS The study retrospectively investigated 161 medical records of CA patients admitted to the Intensive Care Unit (ICU). The random forest classifier algorithm was used to assess the parameters of mortality. The best classification trees were chosen from a set of 100 trees proposed by the algorithm. Conditional mortality odds were investigated with the use of logistic regression models featuring interactions between variables. RESULTS In the logistic regression model, male sex was associated with 5.68-fold higher mortality odds. The mortality odds among the asystole/pulseless electrical activity (PEA) patients were modulated by body mass index (BMI) and among ventricular fibrillation/pulseless ventricular tachycardia (VF/pVT) patients were by serum albumin concentration (decrease by 2.85-fold with 1 g/dl increase). Procalcitonin (PCT) concentration, age, high-sensitivity C-reactive protein (hsCRP), albumin, and potassium were the most influential parameters for mortality prediction with the use of the random forest classifier. Nutritional status-associated parameters (serum albumin concentration, BMI, and Nutritional Risk Score 2002 [NRS-2002]) may be useful in predicting mortality in patients with CA, especially in patients with PCT >0.17 ng/ml, as showed by the decision tree chosen from the random forest classifier based on goodness of fit (AUC score). CONCLUSIONS Mortality in patients following CA is modulated by many co-existing factors. The conclusions refer to sets of conditions rather than universal truths. For individual factors, the 5 most important classifiers of mortality (in descending order of importance) were PCT, age, hsCRP, albumin, and potassium.