Predictive Value Of Risk Factors Research Articles

The prevalence, course, and risk factors of suicidal ideation and suicide attempts among students in vocational education

BackgroundWorldwide, suicide is one of the leading causes of death among adolescents and young adults. Given that suicide in this age group is common within vocational students, this study aims to provide insights into the prevalence, course, and risk factors of suicidal ideation (SI) and suicide attempts (SA) among students in vocational education over the past 10 years.MethodsThis study has a repeated cross-sectional design, utilizing data from 2013 to 2023 provided by the ‘Testjeleefstijl’ foundation in the Netherlands (‘Test Your Lifestyle’). In total, 101,182 students in vocational education completed a web-based standardized questionnaire. Univariate logistic regression was used to test the predictive value of risk factors separately (anxiety and depression, gender, age and school year) on SI and SA. In addition, a machine learning model (Berkelmans et al., 2023) ​was used for high-risk identification of combined risk factors (multivariate models).ResultsWithin vocational students, 12-month SI and SA prevalence increased from respectively 17.7% and 2.3% in schoolyear 2013–2014 to 23% and 3.2% in 2022–2023. Although female gender significantly predicted SI and SA in the univariate analyses, the multivariate models revealed that female gender decreased the likelihood of both SI (OR 0.9) and SA (OR 0.7). A high risk for anxiety and depression was the strongest predictor in the multivariate models for SI (OR 42.8) and SA (OR 19.0).ConclusionOver the past decade, the prevalence of SI and SA increased in students in vocational education, with the risk of anxiety and depression being the strongest contributing factor. While females had a higher prevalence of anxiety and depression, the results suggest these conditions tend to lead to SI and SA more quickly among male students. Early intervention in suicide prevention is crucial, highlighting the need to identify and address anxiety and depression. Vocational education schools have a critical role in this, emphasizing early screening and intervention, with specific attention to gender-specific factors.

Potentially Risk Factors for New Atrial Fibrillation in Patients Undergoing Coronary Artery Bypass Grafting: A Retrospective Cohort Study.

Our study evaluated the risk factors for new postoperative atrial fibrillation (POAF) by analyzing the data collected from patients who underwent first coronary artery bypass grafting (CABG). Our study retrospectively collected data from January 2021 to December 2023 at Changzhi People's Hospital. The perioperative period data were collected, and logistic regression was used to analyze the independent predictors of the occurrence of POAF after CABG and the related predictive values of risk factors were analyzed by using the subjects' work characteristic curve (ROC). A total of 169 patients were included, and there are 45 patients in the POAF group, with an incidence of 26.6%, and 124 in the non-POAF group. The POAF group was significantly higher than the non-POAF group in terms of age (69.2±8.8 years vs 62.3±9.3 years) and preoperative LAD (42.7±7.2mm vs 36.8±5.5mm), and the difference was significant (P<0.05). Preoperative HDL-C in the POAF group were lower than non-POAF group (1.0±0.5 mmol/l vs 1.4±0.7 mmol/l, P<0.05). The logistic regression analysis revealed a significant correlation between age, LAD, HDL-C and the occurrence of POAF (P<0.05). According to the ROC curve analysis, age >64.5 years, LAD >41mm, and HDL-C <0.9 mmol/l were the cut-off values for predicting the occurrence of POAF (AUC1=0.733; AUC2=0.741; AUC3=0.647, P < 0.05). The combined age + LAD + HDL-C (AUC = 0.755; P < 0.05) had a higher diagnostic value and high sensitivity. The age, LAD, and HDL-C are independent risk factors for the POAF after CABG, and clinicians should assess these risk factors as much as possible when managing patients in the perioperative period and make corresponding measures to prevent the development of POAF.

Risk factor analysis and prediction model construction for surgical patients with venous thromboembolism: A prospective study

Abstract Objective: Patients undergoing surgery are at high risk of developing venous thromboembolism (VTE). This study aimed to determine the predictive value of risk factors for VTE in surgical patients and to develop a prediction model by integrating independent predictors. Methods: A total of 1111 patients who underwent surgery at clinical departments in a tertiary general hospital were recruited between May and July 2021. Clinical data, including patient-related, surgery-related, and laboratory parameters, were extracted from the hospital information system and electronic medical records. A VTE prediction model incorporating ten risk variables was constructed using artificial neural networks (ANNs). Results: Ten independent factors (X1: age, X2: alcohol consumption, X3: hypertension, X4: bleeding, X5: blood transfusions, X6: general anesthesia, X7: intrathecal anesthesia, X8: D-dimer, X9: C-reactive protein, and X10: lymphocyte percentage) were identified as associated with an increased risk of VTE. Ten-fold cross-validation results showed that the ANN model was capable of predicting VTE in surgical patients, with an area under the curve (AUC) of 0.89, a Brier score of 0.01, an accuracy of 0.96, and a F1 score of 0.92. The ANN model slightly outperformed the logistic regression model and the Caprini model, but a DeLong test showed that the statistical difference in the AUCs of the ANN and logistic regression models was insignificant (P&gt;0.05). Conclusion: Ten statistical indicators relevant to VTE risk prediction for surgical patients were identified, and ANN and logistic regression both showed promising results as decision-supporting tools for VTE prediction.

Identification and treatment of persistent small airway dysfunction in paediatric patients with asthma: a retrospective cohort study

BackgroundAsthma is a common respiratory disease. In asthma, the small airways have more intensive inflammation and prominent airway remodelling, compared to the central airways. We aimed to investigate the predictive value of risk factors and the fractional concentration of exhaled nitric oxide (FeNO) for persistent small airway dysfunction (p-SAD), and compare the effects of different treatment modalities.MethodsThis retrospective cohort study included 248 children with asthma (aged 4–11 years). Binary logistic regression was used to analyse the risk factors for p-SAD. Correlations among FEV1/FVC, small airway function parameters, and FeNO levels in patients with asthma were analysed using Spearman’s rank correlation. The receiver operating characteristic curve and the Delong test were used to analyse the predictive value of FeNO for p-SAD. Differences in the treatment effects of inhaled corticosteroids (ICS) and ICS with a long-acting beta-agonist (ICS/LABA) on p-SAD were analysed using Fisher’s exact test.ResultsAsthmatic children with older age of receiving the regular treatment (OR 1.782, 95% CI 1.082–2.935), with younger age at the time of onset of suspected asthma symptoms (OR 0.602, 95% CI 0.365–0.993), with longer duration of using ICS or ICS/LABA (OR 1.642, 95% CI 1.170–2.305) and with worse asthma control (OR 3.893, 95% CI 1.699–8.922) had increased risk for p-SAD. Significant negative correlations of small airway function parameters with FeNO at a 200 mL/s flow rate (FeNO200), and the concentration of nitric oxide in the alveolar or acinar region (CaNO) were observed. The areas under the curve of FeNO200 (cut-off:10.5ppb), CaNO (cut-off:5.1ppb), and FeNO200 combined with CaNO were 0.743, 0.697, and 0.750, respectively, for asthma with p-SAD. After using ICS or ICS/LABA, switching to ICS/LABA was easier than continuing with ICS to improve small airway dysfunction (SAD) in the 8th month.ConclusionsPaediatric asthma with p-SAD is associated with older age at receiving regular treatment, younger age at the time of onset of suspected asthma symptoms, longer duration of using ICS or ICS/LABA, worse asthma control, and higher FeNO200 and CaNO levels, all of which can be combined with small airway function indicators to distinguish p-SAD from asthma. ICS/LABA improves SAD better than ICS alone.

Incorporation of emergent symptoms and genetic covariates improves prediction of aromatase inhibitor therapy discontinuation.

Early discontinuation is common among breast cancer patients taking aromatase inhibitors (AIs). Although several predictors have been identified, it is unclear how to simultaneously consider multiple risk factors for an individual. We sought to develop a tool for prediction of AI discontinuation and to explore how predictive value of risk factors changes with time. Survival machine learning was used to predict time-to-discontinuation of AIs in 181 women who enrolled in a prospective cohort. Models were evaluated via time-dependent area under the curve (AUC), c-index, and integrated Brier score. Feature importance was analysis was conducted via Shapley Additive Explanations (SHAP) and time-dependence of their predictive value was analyzed by time-dependent AUC. Personalized survival curves were constructed for risk communication. The best-performing model incorporated genetic risk factors and changes in patient-reported outcomes, achieving mean time-dependent AUC of 0.66, and AUC of 0.72 and 0.67 at 6- and 12-month cutoffs, respectively. The most significant features included variants in ESR1 and emergent symptoms. Predictive value of genetic risk factors was highest in the first year of treatment. Decrease in physical function was the strongest independent predictor at follow-up. Incorporation of genomic and 3-month follow-up data improved the ability of the models to identify the individuals at risk of AI discontinuation. Genetic risk factors were particularly important for predicting early discontinuers. This study provides insight into the complex nature of AI discontinuation and highlights the importance of incorporating genetic risk factors and emergent symptoms into prediction models.

The Utility of Risk Factors to Define Complicated Staphylococcus aureus Bacteremia in a Setting With Low Methicillin-Resistant S. aureus Prevalence.

Recommended duration of antibiotic treatment of Staphylococcus aureus bacteremia (SAB) is frequently based on distinguishing uncomplicated and complicated SAB, and several risk factors at the onset of infection have been proposed to define complicated SAB. Predictive values of risk factors for complicated SAB have not been validated, and consequences of their use on antibiotic prescriptions are unknown. In a prospective cohort, patients with SAB were categorized as complicated or uncomplicated through adjudication (reference definition). Associations and predictive values of 9 risk factors were determined, compared with the reference definition, as was accuracy of Infectious Diseases Society of America (IDSA) criteria that include 4 risk factors, and the projected consequences of applying IDSA criteria on antibiotic use. Among 490 patients, 296 (60%) had complicated SAB. In multivariable analysis, persistent bacteremia (odds ratio [OR], 6.8; 95% confidence interval [CI], 3.9-12.0), community acquisition of SAB (OR, 2.9; 95% CI, 1.9-4.7) and presence of prosthetic material (OR, 2.3; 95% CI, 1.5-3.6) were associated with complicated SAB. Presence of any of the 4 risk factors in the IDSA definition of complicated SAB had a positive predictive value of 70.9% (95% CI, 65.5-75.9) and a negative predictive value of 57.5% (95% CI, 49.1-64.8). Compared with the reference, IDSA criteria yielded 24 (5%) false-negative and 90 (18%) false-positive classifications of complicated SAB. Median duration of antibiotic treatment of these 90 patients was 16 days (interquartile range, 14-19), all with favorable clinical outcome. Risk factors have low to moderate predictive value to identify complicated SAB and their use may lead to unnecessary prolonged antibiotic use.

Analysis of high-risk factors for failure of autologous arteriovenous fistula in hemodialysis patients

To investigate the high risk factors of failure of autologous arteriovenous fistula (AVF) in hemodialysis patients. A retrospective study was conducted, patients with maintenance hemodialysis (MHD) undergoing AVF admitted to General Hospital of Western Theater Command from January 2021 to December 2022 were enrolled, including 107 patients with normal AVF and 168 patients with AVF dysfanction. According to the causes of AVF failure, the patients were divided into AVF stenosis group (n = 103) and AVF thrombosis group (n = 65). Age, gender, body mass index (BMI) and comorbidities (hypertension, diabetes, coronary heart disease) and other clinical data of all patients were collected. Hemoglobin, hematocrit, white blood cell count, neutrophil count, lymphocyte count, platelet count, C-reactive protein (CRP), high density lipoprotein, low density lipoprotein, neutrophil/lymphocyte ratio (NLR), platelet/lymphocyte ratio (PLR) within 1 month of AVF use in normal dialysis patients and 1 week before AVF failure. Multivariate Logistic regression was used to analyze the independent risk factors of AVF dysfuction in MHD patients. The receiver operator characteristic curve (ROC curve) was drawn to evaluate the predictive value of risk factors on AVF dysfuction in MHD patients. (1) There were significant differences in age, BMI, hypertension, hemoglobin, hematocrit, PLR and CRP [age (years): 56.94±14.32, 58.83±14.05, 51.57±13.19; BMI (kg/m2): 22.83±3.10, 21.27±4.98, 23.35±2.72; hypertension: 93.20%, 64.62%, 86.92%; hemoglobin (g/L): 110.82±22.16, 88.70±24.00, 87.95±23.45; hematocrit: 0.350±0.069, 0.282±0.076, 0.275±0.071; PLR: 197.35±113.59, 192.55±138.25, 162.12±73.25; CRP (mg/L): 10.01±4.02, 8.18±5.42, 3.17±1.30, all P < 0.05], among AVF stenosis group, AVF thrombosis group and AVF normal group, there were statistically significant differences no statistically significant difference was found in other indexes among three groups. (2) Multivariate Logistic regression analysis showed that hypertension [odds ratio (OR) = 4.849, 95% confidence interval (95%CI) was 1.278-18.397, P = 0.020], elevated CRP levels (OR = 2.104, 95%CI was 1.533-2.888, P = 0.000) were associated with AVF stenosis. Elevated CRP levels (OR = 1.984, 95%CI was 1.442-2.730, P = 0.000) was an independent risk factor for AVF thrombosis. Analysis of ROC curve showed that the area under the curve (AUC) of AVF dysfunction predicted by CRP was 0.712, 95%CI was 0.637-0.786, P = 0.000; CRP cut-off value was 1.8 mg/L, the sensitivity was 67.0%, the specificity was 83.7%. Elevated CRP is an independent risk factor for AVF failure in hemodialysis patients, which can be used to predict the occurrence of AVF failure.

Associated Biochemical and Hematological Markers in COVID-19 Severity Prediction.

The global threat of COVID-19 has created the need for researchers to investigate the disease's progression, especially through the use of biomarkers to inform interventions. This study aims to assess the correlations of laboratory parameters to determine the severity of COVID-19 infection. This study was conducted among 191 COVID-19 patients in Sumeru Hospital, Lalitpur, Nepal. According to their clinical outcomes, these patients were divided into severe and nonsevere groups. Inflammatory markers such as LDH, D-dimer, CRP, ferritin, complete blood cell count, liver function tests, and renal function tests were performed. Binary logistic regression analysis determined relative risk factors associated with severe COVID-19. The area under the curve (AUC) was calculated with ROC curves to assess the potential predictive value of risk factors. Out of 191 patients, 38 (19.8%) subjects died due to COVID-19 complications, while 156 (81.7%) survived and were discharged from hospital. The COVID-19 severity was found in patients with older age and comorbidities such as CKD, HTN, DM, COPD, and pneumonia. Parameters such as d-dimer, CRP, LDH, SGPT, neutrophil, lymphocyte count, and LMR were significant independent risk factors for the severity of the disease. The AUC was highest for d-dimer (AUC = 0.874) with a sensitivity of 82.2% and specificity of 81.2%. Similarly, the cut-off values for other factors were age >54.5 years, D-dimer >0.91 ng/ml, CRP >82.4 mg/dl, neutrophil >78.5%, LDH >600 U/L, and SGPT >35.5 U/L, respectively. Endorsement of biochemical and hematological parameters with their cut-off values also aids in predicting COVID-19 severity. The biomarkers such as D-dimer, CRP levels, LDH, ALT, and neutrophil count could be used to predict disease severity. So, timely analysis of these markers might allow early prediction of disease progression.

Modified universal new-born hearing screening: Practical approach for developing world (Georgean experience).

Analysis of newborn hearing screening (NBS) outcomes and proposing a modified strategy for better performance of NBS in developing world. Descriptive (retrospective and prospective) study. A total of 8412 newborns/neonates were subjected to risk factor assessment followed by a set of subjective (behavioral audiometry: BoA) and objective (OAE: otoacoustic emissions, ABR: auditory brainstem-evoked responses) hearing-screening in different combinations. DPOAE (primary objective tool) was undertaken in 2565 cases. Predictive value of risk factors on deafness was statistically analysed along with affectivity of objective, subjective and questionnaire-based screening tools. Amongst 8412 babies, 45.5% were 'at risk' (major 36.41%, minor 9.17%). The OAE was abnormal in 299 cases (11.6% of 2565 & 3.5% in 8412). The abnormal OAE rate in prospective cases was 3.5% while in retrospective cases that underwent initial screening with BoA was 41%. A significant correlation was seen with consanguinity, high blood pressure, NICU stay>5 days, low birth weight, neonatal jaundice, delayed birth cry, pre-mature status, birth asphyxia and maternal excessive vomiting, while regression models of OAE outcome (sensitivity 99%, accuracy 89%) revealed highest predictive value for the initial 3 factors. BoA-screening revealed a sensitivity of 72.6%, and negative predictive value of 42.4%. Also a perfect correlation was evident between OAE-ABR, OAE-BoA and ABR-BoA. A subjective NBS screening through questionnaire-based-risk-assessment and modified-BoA followed by selective referral for objective assessment is more practical and focussed approach for poor resourced countries that is likely to screen a larger population.

Predictive model for diabetes mellitus occurrence in Iran’s southeastern region: a study based on American diabetes association guidelines

To control diabetes in a society, risk assessment tools are used to predict disease risk. We aimed to assess the value of different risk factors for diabetes mellitus in a remarkable community in the city of Kerman, one of the vast areas in the southeast of Iran, with the final goal of designing a predictive model for diabetes in this region. This study was a cross-sectional study with the aim of investigating the predictive value of risk factors indicating the presence of diabetes in the population of Kerman City based on the guidelines of the American Diabetes Association (ADA) risk assessment tool. The information of 4000 people participating in the comprehensive screening plan for cardiovascular risk factors in Kerman City was extracted by reviewing the relevant data registry. According to the ADA guideline, 32.5% of participants were at risk for diabetes mellitus. The hazard ratio of diabetes mellitus in the subgroup with the ADA final score ≥5 as compared to those with a lower final score was 1.9. Advanced age, history of gestational diabetes, family history of diabetes mellitus, history of hypertension, low physical activity, and higher body mass index were the main determinants of diabetes mellitus. According to ADA guidelines and the diabetes mellitus risk assessment tool, 32.5% of the population residents in Kerman City are potentially at risk for diabetes mellitus that can be successfully predicted aide by the ADA risk assessment tool.

Predictive effects of S100β and CRP levels on hemorrhagic transformation in patients with AIS after intravenous thrombolysis: A concise review based on our center experience.

Hemorrhagic transformation (HT) is one of the most dangerous complications after intravenous thrombolysis in patients with acute ischemic stroke (AIS). Therefore, we want to explore the predictive effects of peripheral blood S100β and C-Reactive Protein (CRP) levels on hemorrhagic transformation after intravenous thrombolysis in AIS patients. Ninety-two AIS patients who had been treated in Huai'an Second People's Hospital from January 2018 to December 2021 were retrospectively selected. Patients were divided into hemorrhagic transformation (HT) groups (24 cases) and no HT groups (68 cases) based on whether there was hemorrhagic transformation within 24 h after intravenous thrombolysis. General clinical data from the HT group and no HT group were compared. A multivariate logistic regression model was used to analyze the potential risk factors of HT after intravenous thrombolysis in patients with AIS. A receiver operating curve (ROC) was used to analyze the predictive value of risk factors for HT. High serum S100β, CRP levels, and National Institutes of Health Stroke Scale (NIHSS) scores were found to be risk factors for HT after intravenous thrombolysis in patients with AIS (all P < .05). The ROC curve analysis showed that critical value of S100β, CRP level, and NIHSS score for predicting intravenous thrombolytic HT in AIS patients were 0.335, 8.700, and 14.50, respectively, and their sensitivities were 0.750, 0.971, and 0.333 (P < .05), respectively. High serum S100β and CRP levels are risk factors for HT after intravenous thrombolysis in AIS patients and have predictive influence of the occurrence of HT in AIS patients.

Predictive value of risk factors for bladder dysfunction in Chinese patients with type 2 diabetes mellitus: A case-control study.

To analyze risk factors associated with bladder dysfunction in patients with type 2 diabetes mellitus (T2DM) and to construct a prediction model for early prediction of diabetic bladder dysfunction (DBD). We included hospitalized patients with T2DM from the endocrinology department of Shenzhen Hospital, Southern Medical University, Shenzhen, China, from January 2019 to 2022. Factors associated with DBD in bivariate analysis with a p < 0.05 were included in a multivariate logistic regression analysis. Multivariate logistic regression analysis was used to determine independent risk factors and to construct a prediction model. The prediction model was presented as the model formula. The receiver operating characteristic (ROC) curve was used to evaluate the predictive value of the above risk factors and the prediction model for DBD. The model was internally verified by Boostrap resampling 1000 times. Two hundred and eleven patients were included in this study, and they were divided into the DBD group (n = 101) and the non-DBD group (n = 110). Eight variables showed significant significance in the bivariate analysis, including age, diabetic peripheral neuropathy (DPN), glycated hemoglobin (HbA1c), urinary microalbumin (mALB), red blood cell count (RBC), white blood cell count (WBC),absolute neutrophil count (ANC), percentage of monocyte (Mono%). Furthermore, multivariate logistic regression analysis revealed that age (OR [95%CI]: 1.077 [1.042-1.112]), p < 0.001; DPN (OR [95%CI]: 2.373 [1.013-5.561]), p = 0.047; HbA1c (OR [95%CI]: 1.170 [1.029-1.330]), p = 0.017 and ANC (OR [95%CI]: 1.234 [1.059-1.438]), p = 0.007 were independent risk factors for the DBD. The prediction model formula was Logit (p) = -6.611 + 0.074 age + 0.864 DPN + 0.157 HbA 1 c + 0.078 ANC. The area under the ROC curve (AUC) for the four risk factors were 0.676, 0.582, 0.618, and 0.674, respectively. The prediction model predicted DBD with higher accuracy than the individual risk factors, AUC = 0.817 (95%CI: 0.757-0.877), and the sensitivity and specificity were 88.1% and 50.0%, respectively. The model internal validation results showed that the AUC = 0.804 (95%CI: 0.707-0.901), and the calibration curve is close to the ideal diagonal line. Age, DPN, HbA1c, and ANC were risk factors for DBD. The prediction model constructed based on the four risk factors had a good predictive value for predicting the occurrence of DBD.

Recovery trajectories of IQ after pediatric TBI: A latent class growth modeling analysis.

To identify latent trajectories of IQ over time after pediatric traumatic brain injury (TBI) and examine the predictive value of risk factors within and across recovery trajectories. 206 children ages 3-7 years at injury were included: 87 TBI (23 severe, 21 moderate, 43 complicated mild) and 119 orthopedic injury (OI). We administered intelligence tests shortly after injury (1½ months), 12 months, and 6.8 years postinjury. Latent class growth modeling was used to identify latent subgroups. Separate models examined verbal and nonverbal IQ recovery trajectories following TBI versus OI. Variables included: age at injury, sex, race, socioeconomic status, injury severity, quality of the home environment, family functioning, and parenting style. Both the TBI and OI analyses yielded different growth models for nonverbal (k = 3) and verbal IQ (k = 3). Although all models resulted in 3 latent classes (below average, average, and aboveaverage performance); trajectory shapes, contributors to class membership, and performance within each class varied by injury group and IQ domain. TBI severity was associated with class membership for nonverbal IQ, with less severe injuries associated with higher IQ scores; however, TBI severity did not influence verbal IQ class membership. Parenting style had a more prominent effect on verbal and nonverbal IQ within the TBI than OI trajectories. Findings suggest TBI severity is related to recovery trajectories for nonverbal but not verbal IQ and parenting style has stronger effects on recovery in TBI than OI. Results highlight the importance of parental factors on long-term recovery after TBI.

Risk factors for ischemic stroke in men and women aged 45–74 years

Stroke is one of the leading causes of death and primary disability and contributes significantly to the global burden of disease. Identification of individuals at high risk of ischemic stroke (IS) is an important component of disease prevention. The differences between risk factors (RF) for stroke in men and women are not well understood.Objective: to determine the predictive value of RF for the development of IS in men and women aged 45–74 years.Material and methods. The study included 728 patients (380 men and 348 women) aged 45–74 years, including 392 patients (247 men and 145 women) with IS in the carotid artery basin (main group) and 336 patients (133 men and 203 women) with vascular cognitive impairment (comparison group). To build predictive models to assess the impact of RF on the development of IS in men and women, we used the logistic regression method with stepwise exclusion of variables according to the Wald algorithm.Results. The predictive regression model of IS in men includes atherosclerotic stenosis of the internal carotid arteries [odds ratio (OR) 3.571; 95% confidence interval (CI) 1.792–7.114], in a predictive regression model in women – diabetes mellitus type 2 (OR 5.074; 95% CI 1.768–14.561) and a history of IS (OR 6.857; 95% CI 1.825–25.762). Other risk factors for the development of IS (atrial fibrillation, history of transient ischemic attack, arterial hypertension stage) affected both men and women.Conclusion. Atherosclerotic stenosis of the internal carotid arteries was found to be a sex-specific prognostic factor for IS in men, and type 2 diabetes mellitus and a history of IS in women.

The Predictive Value of Risk Factors and Prognostic Scores in Hospitalized COVID-19 Patients.

Risk stratification in patients with COVID-19 is a challenging task. Early warning scores (EWSs) are commonly used tools in the initial assessment of critical patients. However, their utility in patients with COVID-19 is still undetermined. This study aimed to discover the most valuable predictive model among existing EWSs for ICU admissions and mortality in COVID-19 patients. This was a single-center cohort study that included 3608 COVID-19 patients admitted to the University Clinical Hospital Center Bezanijska Kosa, Belgrade, Serbia, between 23 June 2020, and 14 April 2021. Various demographic, laboratory, and clinical data were collected to calculate several EWSs and determine their efficacy. For all 3608 patients, five EWSs were calculated (MEWS, NEWS, NEWS2, REMS, and qSOFA). Model discrimination performance was tested using sensitivity, specificity, and positive and negative predictive values. C statistic, representing the area under the receiver operating characteristic (ROC) curve, was used for the overall assessment of the predictive model. Among the evaluated prediction scores for 3068 patients with COVID-19, REMS demonstrated the highest diagnostic performance with the sensitivity, PPV, specificity, and NPV of 72.1%, 20.6%, 74.9%, and 96.8%, respectively. In the multivariate logistic regression analysis, aside from REMS, age (p < 0.001), higher CT score (p < 0.001), higher values of urea (p < 0.001), and the presence of bacterial superinfection (p < 0.001) were significant predictors of mortality. Among all evaluated EWSs to predict mortality and ICU admission in COVID-19 patients, the REMS score demonstrated the highest efficacy.

Interpretable machine learning to predict adverse perinatal outcomes: examining marginal predictive value of risk factors during pregnancy

The timely identification of nulliparas at high risk of adverse fetal and neonatal outcomes during pregnancy is crucial for initiating clinical interventions to prevent perinatal complications. Although machine learning methods have been applied to predict preterm birth and other pregnancy complications, many models do not provide explanations of their predictions, limiting the clinical use of the model. This study aimed to develop interpretable prediction models for a composite adverse perinatal outcome (stillbirth, neonatal death, estimated Combined Apgar score of <10, or preterm birth) at different points in time during the pregnancy and to evaluate the marginal predictive value of individual predictors in the context of a machine learning model. This was a secondary analysis of the Nulliparous Pregnancy Outcomes Study: Monitoring Mothers-to-be data, a prospective cohort study in which 10,038 nulliparous pregnant individuals with singleton pregnancies were enrolled. Here, interpretable prediction models were developed using L1-regularized logistic regression for adverse perinatal outcomes using data available at 3 study visits during the pregnancy (visit 1: 6 0/7 to 13 6/7 weeks of gestation; visit 2: 16 0/7 to 21 6/7 weeks of gestation; visit 3: 22 0/7 to 29 6/7 weeks of gestation). We identified the important predictors for each model using SHapley Additive exPlanations, a model-agnostic method of computing explanations of model predictions, and evaluated the marginal predictive value of each predictor using the DeLong test. Our interpretable machine learning model had an area under the receiver operating characteristic curves of 0.617 (95% confidence interval, 0.595-0.639; all predictor variables at visit 1), 0.652 (95% confidence interval, 0.631-0.673; all predictor variables at visit 2), and 0.673 (95% confidence interval, 0.651-0.694; all predictor variables at visit 3). For all visits, the placental biomarker inhibin A was a valuable predictor, as including inhibin A resulted in better performance in predicting adverse perinatal outcomes (P<.001, all visits). At visit 1, endoglin was also a valuable predictor (P<.001). At visit 2, free beta human chorionic gonadotropin (P=.001) and uterine artery pulsatility index (P=.023) were also valuable predictors. At visit 3, cervical length was also a valuable predictor (P<.001). Despite various advances in predictive modeling in obstetrics, the accurate prediction of adverse perinatal outcomes remains difficult. Interpretable machine learning can help clinicians understand how predictions are made, but barriers exist to the widespread clinical adoption of machine learning models for adverse perinatal outcomes. A better understanding of the evolution of risk factors for adverse perinatal outcomes throughout pregnancy is necessary for the development of effective interventions.

A single-center clinical study of acute kidney injury associated with acute myocardial infarction.

To investigate the risk factors of acute kidney injury (AKI) patients with acute myocardial infarction (AMI) and establish potential microRNA (miRNA) biomarkers in the peripheral blood of AMI-AKI patients. Patients hospitalized from 2016 to 2020 and diagnosed with AMI (with AKI or without AKI groups) were recruited. The data of the two groups were compared and the risk factors of AMI-AKI were analyzed by logistic regression. The receiver operator characteristics (ROC) curve was drawn and the predictive value of risk factors in AMI-AKI was evaluated. Six AMI-AKI patients were selected and six healthy subjects were enrolled as the control. The peripheral blood samples of the two groups were collected for miRNA high-throughput sequencing. A total of 300 AMI patients were collected, including 190 patients with AKI and 110 patients without AKI. Multivariate logistic regression analysis indicated that diastolic pressure (68-80mmHg), urea nitrogen, creatinine, serum uric acid (SUA), aspartate aminotransferase (AST), and left ventricular ejection fraction were the dependent risk factors of AMI-AKI patients (P < 0.05). ROC curve showed that the incidence of AMI-AKI patients was most correlated with urea nitrogen, creatinine, and SUA. In addition, 60 differentially expressed miRNAs were identified between AMI-AKI and controls. Then, hsa-miR-2278, hsa-miR-1827, and hsa-miR-149-5p were more corrected with predictors. Twelve of them targeted 71 genes involved in phagosome, oxytocin signaling pathway, and microRNAs in cancer pathways. Urea nitrogen, creatinine, and SUA were the dependent risk factors and important predictors for AMI-AKI patients. Three miRNAs may be considered as biomarkers for AMI-AKI.

Predictive value of risk factors for prognosis of patients with sepsis in intensive care unit.

Sepsis has emerged as a major global public health concern due to its elevated mortality and high cost of care. This study aimed to evaluate the risk factors associated with the mortality of sepsis patients in the Intensive Care Unit (ICU), and to intervene in the early stages of sepsis in order to improve patient outcomes and reduce mortality. From January 1st, 2021 to December 31st, 2021, Longhua Hospital Affiliated to Shanghai University of Traditional Chinese Medicine, Huashan Hospital Affiliated to Fudan University, and The Seventh People's Hospital Affiliated to Shanghai University of Traditional Chinese Medicine were designated as sentinel hospitals, and sepsis patients in their respective ICU and Emergency ICU were selected as research subjects, and divided into survivors and non-survivors according to their discharge outcomes. The mortality risk of sepsis patients was subsequently analyzed by logistic regression. A total of 176 patients with sepsis were included, of which 130 (73.9%) were survivors and 46 (26.1%) were non-survivors. Factors identified as having an impact on death among sepsis patients included female [Odds Ratio (OR) = 5.135, 95% confidence interval (CI): 1.709, 15.427, P = .004)], cardiovascular disease (OR = 6.272, 95% CI: 1.828, 21.518, P = .004), cerebrovascular disease (OR = 3.133, 95% CI: 1.093, 8.981, P = .034), pulmonary infections (OR = 6.700, 95% CI: 1.744, 25.748, P = .006), use of vasopressors (OR = 34.085, 95% CI: 10.452, 111.155, P < .001), WBC < 3.5 × 109/L (OR = 9.752, 95% CI: 1.386, 68.620, P = .022), ALT < 7 U/L (OR = 7.672, 95% CI: 1.263, 46.594, P = .027), ALT > 40 U/L (OR = 3.343, 95% CI: 1.097, 10.185, P = .034). Gender, cardiovascular disease, cerebrovascular disease, pulmonary infections, the use of vasopressors, WBC, and ALT are important factors in evaluating the prognostic outcome of sepsis patients in the ICU. This suggests that medical professionals should recognize them expeditiously and implement aggressive treatment tactics to diminish the mortality rate and improve outcomes.

Prevalence and Risk Factors of Metabolic-Associated Fatty Liver Disease Among Hospital Staff.

The prevalence of metabolism-related fatty liver disease (MAFLD) has been rarely reported in hospital staffs. The aim of this study was to assess the prevalence and risk factors for MAFLD in hospital staffs aged ≥18 years. Based on type B ultrasonic, hospital staffs who underwent medical examinations at the second Affiliated Hospital of Hainan Medical University from January 2022 to March 2022 were classified into health control group (661 subjects) and MAFLD group (223 subjects), demographic, biochemical and blood examination information were compared between 2 groups. Independent risk factors for MAFLD were determined by logistic regression. Predictive values of risk factors of MAFLD were evaluated by receiver operating characteristic (ROC) curves. The prevalence of MAFLD was 33.7%. Older age (OR=1.08, p<0.001), H. pylori infection (OR=0.234, p=0.02), triglyceride-glucose (TyG) (OR=7.001, p<0.001), low-density lipoprotein cholesterol (LDL-C) (OR=2.076, p=0.028), red blood cell (RBC) (OR=2.386, p=0.001), eating out (OR=0.048, p=0.001), regular exercise (OR=23.017, p<0.001), and overweight (OR=3.891, p=0.003) were independently associated factors for MAFLD. The AUC of model predicting MAFLD is 0.910 [95% CI (0.886, 0.934)], with 0.794 sensitivity, 0.908 specificity. The diagnostic value of model was higher in the female MAFLD group after stratified analysis according to gender. The model showed that TyG was the factor contributing more to MAFLD. The diagnostic value of TyG was higher in the female MAFLD group than male MAFLD group. The prevalence of MAFLD among hospital staffs was 33.7%. TyG can be used to predict MAFLD especially for female hospital staffs for early intervention.

Factors influencing pulmonary infection in elderly neurocritical patients and their predictive values: a data analysis for consecutive four-year

To analyze the factors influencing pulmonary infections in elderly neurocritical patients in the intensive care unit (ICU) and to explore the predictive value of risk factors for pulmonary infections. The clinical data of 713 elderly neurocritical patients [age ≥ 65 years, Glasgow coma score (GCS) ≤ 12 points] admitted to the department of critical care medicine of the Affiliated Hospital of Guizhou Medical University from 1 January 2016 to 31 December 2019 were retrospectively analyzed. According to whether or not they had HAP, the elderly neurocritical patients were divided into hospital-acquired pneumonia (HAP) group and non-HAP group. The differences in baseline data, medication and treatment, and outcome indicators between the two groups were compared. Logistic regression analysis was used to analyze the factors influencing the occurrence of pulmonary infection. The receiver operator characteristic curve (ROC curve) was plotted for risk factors and a predictive model was constructed to evaluate the predictive value for pulmonary infection. A total of 341 patients were enrolled in the analysis, including 164 non-HAP patients and 177 HAP patients. The incidence of HAP was 51.91%. According to univariate analysis, compared with the non-HAP group, mechanical ventilation time, the length of ICU stay and total hospitalization in the HAP group were significantly longer [mechanical ventilation time (hours): 171.00 (95.00, 273.00) vs. 60.17 (24.50, 120.75), the length of ICU stay (hours): 263.50 (160.00, 409.00) vs. 114.00 (77.05, 187.50), total hospitalization (days): 29.00 (13.50, 39.50) vs. 27.00 (11.00, 29.50), all P < 0.01], the proportion of open airway, diabetes, proton pump inhibitor (PPI), sedative, blood transfusion, glucocorticoids, and GCS ≤ 8 points were significantly increased than those in HAP group [open airway: 95.5% vs. 71.3%, diabetes: 42.9% vs. 21.3%, PPI: 76.3% vs. 63.4%, sedative: 93.8% vs. 78.7%, blood transfusion: 57.1% vs. 29.9%, glucocorticoids: 19.2% vs. 4.3%, GCS ≤ 8 points: 83.6% vs. 57.9%, all P < 0.05], prealbumin (PA) and lymphocyte count (LYM) decreased significantly [PA (g/L): 125.28±47.46 vs. 158.57±54.12, LYM (×109/L): 0.79 (0.52, 1.23) vs. 1.05 (0.66, 1.57), both P < 0.01]. Logistic regression analysis showed that open airway, diabetes, blood transfusion, glucocorticoids and GCS ≤ 8 points were independent risk factors for pulmonary infection in elderly neurocritical patients [open airway: odds ratio (OR) = 6.522, 95% confidence interval (95%CI) was 2.369-17.961; diabetes: OR = 3.917, 95%CI was 2.099-7.309; blood transfusion: OR = 2.730, 95%CI was 1.526-4.883; glucocorticoids: OR = 6.609, 95%CI was 2.273-19.215; GCS ≤ 8 points: OR = 4.191, 95%CI was 2.198-7.991, all P < 0.01], and LYM, PA were the protective factors for pulmonary infection in elderly neurocritical patients (LYM: OR = 0.508, 95%CI was 0.345-0.748; PA: OR = 0.988, 95%CI was 0.982-0.994, both P < 0.01). ROC curve analysis showed that the area under the ROC curve (AUC) for predicting HAP using the above risk factors was 0.812 (95%CI was 0.767-0.857, P < 0.001), with a sensitivity of 72.3% and a specificity of 78.7%. Open airway, diabetes, glucocorticoids, blood transfusion, GCS ≤ 8 points are independent risk factors for pulmonary infection in elderly neurocritical patients. The prediction model constructed by the above mentioned risk factors has certain predictive value for the occurrence of pulmonary infection in elderly neurocritical patients.