High Predictive Value Research Articles

Cerebrospinal Creatine Kinase BB Isoenzyme: A Biomarker for Predicting Outcome After Cardiac Arrest.

Cerebrospinal fluid creatine kinase BB isoenzyme (CSF CK-BB) after cardiac arrest (CA) has been shown to have a high positive predictive value for poor neurological outcome, but it has not been evaluated in the setting of targeted temperature management (TTM) and modern CA care. We aimed to evaluate CSF CK-BB as a prognostic biomarker after CA. We performed a retrospective cohort study of patients with CA admitted between 2010 and 2020 to a three-hospital health system who remained comatose and had CSF CK-BB assayed between 36 and 84h after CA. We examined the proportion of patients at hospital discharge who achieved favorable or intermediate neurological outcome, defined as Cerebral Performance Category score of 1-3, compared with those with poor outcome (Cerebral Performance Category score 4-5) for various CSF CK-BB thresholds. We also evaluated additive value of bilateral absence of somatosensory evoked potentials (SSEPs). Among 214 eligible patients, the mean age was 54.7 ± 4.8years, 72% of patients were male, 33% were nonwhite, 17% had shockable rhythm, 90% were out-of-hospital CA, and 83% received TTM. A total of 19 (9%) awakened. CSF CK-BB ≥ 230 U/L predicted a poor outcome at hospital discharge, with a specificity of 100% (95% confidence interval [CI] 82-100%) and sensitivity of 69% (95% CI 62-76%). When combined with bilaterally absent N20 response on SSEP, specificity remained 100% while sensitivity increased to 80% (95% CI 73-85%). Discordant CK-BB and SSEP findings were seen in 13 (9%) patients. Cerebrospinal fluid creatine kinase BB isoenzyme levels accurately predicted poor neurological outcome among CA survivors treated with TTM. The CSF CK-BB cutoff of 230 U/L optimizes sensitivity to 69% while maintaining a specificity of 100%. CSF CK-BB could be a useful addition to multimodal neurological prognostication after CA.

A moderately higher time-in-range threshold improves the prognosis of type 2 diabetes patients complicated with COVID-19.

After fully lifting coronavirus disease 2019 (COVID-19) pandemic control measures in mainland China in 12/2022, the incidence of COVID-19 has increased markedly, making it difficult to meet the general time-in-range (TIR) requirement. We investigated a more clinically practical TIR threshold and examined its association with the prognosis of COVID-19 patients with type 2 diabetes(T2D). 63 T2D patients complicated with COVID-19 were evaluated. Patients were divided into favorable outcome group and adverse outcome group according to whether achieving composite endpoint (a >20-day length of stay, intensive care unit admission, mechanical ventilation use, or death). TIR, the time-below-range (TBR) and the time-above-range (TAR) were calculated from intermittently scanned continuous glucose monitoring. Logistic regression analysis and other statistical methods were used to analyze the correlation between glucose variability and prognosis to establish the appropriate reference range of TIR. TIR with thresholds of 80 to 190 mg/dL was significantly associated with favorable outcomes. An increase of 1% in TIR is connected with a reduction of 3.70% in the risk of adverse outcomes. The Youden index was highest when the TIR was 54.73%, and the sensitivity and specificity were 58.30% and 77.80%, respectively. After accounting for confounding variables, our analysis revealed that threshold target ranges (TARs) ranging from 200 mg/dL to 230 mg/dL significantly augmented the likelihood of adverse outcomes. The TIR threshold of 80 to 190 mg/dL has a comparatively high predictive value of the prognosis of COVID-19. TIR >54.73% was associated with a decreased risk of adverse outcomes. These findings provide clinically critical insights into possible avenues to improve outcomes for COVID-19 patients with T2D.

The Value of Serum Procalcitonin, Thromboelastography Combined with Platelet Count in Predicting the Short-Term Progression of Septic Shock in the Intensive Care Unit.

By evaluating the level of serum procalcitonin (PCT), thromboelastography (TEG) and platelet count (PLT) of patients with septic shock in intensive care unit (ICU), the predictive value of the combination of the three indicators on the short-term progression was discussed, which provided a new basis for early clinical diagnosis and disease evaluation. The clinical data of 130 patients with septic shock admitted to the IUC of our hospital from December 2021 to December 2023 were analyzed retrospectively. These subjects were divided into good prognosis group (n=78) and poor prognosis group (n=52) according to the 28 d deaths. The influencing factors were explored using the Multivariate logistic regression analysis. The value of single or combined PCT, PLT and TEG in predicting poor short-term prognosis was assessed using the receiver operating characteristic (ROC) curve. The patients in poor prognosis group had higher Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, serum PCT level, coagulation reaction time (R value) and coagulation formation time (K value), but lower PLT levels, final strength of coagulation (MA value) and coagulation formation rate (α angle) than those in good prognosis group (P<0.001). PCT, R value and K value were risk factors (P<0.001), while PLT, MA value and α angle were protective factors (P<0.001). The area under the curve (AUC) of PCT, PLT and TEG predicting poor short-term progression was 0.813, 0.658 and 0.752, respectively. The AUC of combined three indicators was 0.905, which had the highest predictive value. Serum levels of PCT, PLT and TEG had certain value in predicting poor short-term progression of septic shock patients, and their combined diagnostic value was higher. Therefore, regular monitoring of these three indicators could provide certain guiding significance for the prevention and treatment of poor short-term prognosis in patients with septic shock.

Correlations of high miRNA expressions with traditional proteins and prognosis of breast cancer

Abstract Background In this study, we aimed to analyze the correlations of high expressions of micro ribonucleic acids (miRNAs) with traditional proteins and prognosis of breast cancer. Methods The clinical data of 60 breast cancer patients treated from August 2018 to July 2019 were retrospectively analyzed. All the patients received radical mastectomy combined with postoperative adjuvant chemoradiotherapy and were followed up for 3 years after treatment. The 3-year survival was recorded. The surviving patients were included in a good prognosis group, and the deceased ones were assigned to a poor prognosis group. The levels of miRNAs (miR-182, miR-155, and miR-217) and traditional breast cancer proteins [estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor-2 (CerbB-2/HER-2), and cell proliferation factor-67 (Ki-67)] were measured. Receiver operating characteristic (ROC) curve was plotted to investigate the predictive value of miRNA levels for poor prognosis. Results The relative expressions of miR-182, miR-155 and miR-217 were negatively correlated with ER and PR (r&lt;0, P&lt;0.05), but positively correlated with positive Ki-67 expression (r&gt;0, P&lt;0.05). High relative expressions of miR-182, miR-155 and miR-217 and positive Ki-67 expression were risk factors for poor prognosis, and the positive expressions of ER and PR were protective factors (OR&lt;1, P&lt;0.05). The areas under the curves of the relative expressions of miR-182, miR-155 and miR-217 and combined detection for predicting poor prognosis all exceeded 0.70. The combined detection had the highest predictive value. Conclusions The high expressions of miR-182, miR-155 and miR-217 are correlated with the expressions of traditional breast cancer proteins ER, PR, and Ki-67, and may predict the prognosis of breast cancer patients.

The role of basophil activation test in venom ımmunotherapy: comparative evaluation with specific IgE and skin prick tests, ınnovative approaches.

Background. In diagnosing insect venom allergy and making immunotherapy decisions, clinical history, skin tests, and specific serum IgE levels are commonly utilized. This study aims to emphasize the clinical significance of using the basophil activation test in accurately identifying sensitivities in individuals with insect venom allergy and to compare its effectiveness with other testing methods. Methods. This study included a total of 43 patients, who experienced at least one systemic allergic reaction following insect stings and were deemed suitable for immunotherapy.Basophil activation test, specific serum IgE levels, and skin prick test results utilized in making immunotherapy treatment decisions were recorded. Results. Our study determined that the overall clinical sensitivities of the basophil activation test (BAT), specific serum IgE (spIgE), and skin prick test (SPT) for apis mellifera were 95.5%, 95.7%, and 48.4% respectively, while for vespula vulgaris, they were 83.3%, 100%, and 33.3%. Based on these results, the prediction of systemic reactions to bee stings is ordered as spIgE > BAT > SPT. Additionally, early-stage skin prick tests showed a sensitivity of 67% and specificity of 50% at a cut-off value of 1.5 mm, and 33% sensitivity and 83% specificity at 2.5 mm. Conclusions. This study demonstrates that the basophil activation test (BAT) can provide a high positive predictive value in immunotherapy treatment decisions and offer significant insights in clinical practices.

Predictive value of delta-radiomic features for prognosis of advanced non-small cell lung cancer patients undergoing immune checkpoint inhibitor therapy.

No robust predictive biomarkers exist to identify non-small cell lung cancer (NSCLC) patients likely to benefit from immune checkpoint inhibitor (ICI) therapies. The aim of this study was to explore the role of delta-radiomics features in predicting the clinical outcomes of patients with advanced NSCLC who received ICI therapy. Data of 179 patients with advanced NSCLC (stages IIIB-IV) from two institutions (Database 1 =133; Database 2 =46) were retrospectively analyzed. Patients in the Database 1 were randomly assigned into training and validation dataset, with a ratio of 8:2. Patients in Database 2 were allocated into testing dataset. Features were selected from computed tomography (CT) images before and 6-8 weeks after ICI therapy. For each lesion, a total of 1,037 radiomic features were extracted. Lowly reliable [intraclass correlation coefficient (ICC) <0.8] and redundant (r>0.8) features were excluded. The delta-radiomics features were defined as the relative net change of radiomics features between two time points. Prognostic models for progression-free survival (PFS) and overall survival (OS) were established using the multivariate Cox regression based on selected delta-radiomics features. A clinical model and a pre-treatment radiomics model were established as well. The median PFS (after therapy) was 7.0 [interquartile range (IQR): 3.4, 9.1] (range, 1.4-13.2) months. To predict PFS, the model established based on the five most contributing delta-radiomics features yielded Harrell's concordance index (C-index) values of 0.708, 0.688, and 0.603 in the training, validation, and testing databases, respectively. The median survival time was 12 (IQR: 8.7, 15.8) (range, 2.9-23.3) months. To predict OS, a promising prognostic performance was confirmed with the corresponding C-index values of 0.810, 0.762, and 0.697 in the three datasets based on the seven most contributing delta-radiomics features, respectively. Furthermore, compared with clinical and pre-treatment radiomics models, the delta-radiomics model had the highest area under the curve (AUC) value and the best patients' stratification ability. The delta-radiomics model showed a good performance in predicting therapeutic outcomes in advanced NSCLC patients undergoing ICI therapy. It provides a higher predictive value than clinical and the pre-treatment radiomics models.

The predictive value of CHA2DS2-VASc score on the prognosis of patients with atrial fibrillation based on a prospective cohort study.

Atrial fibrillation (AF) is the most prevalent cardiac arrhythmia encountered in clinical practice, and it is associated with an increased risk of mortality, stroke, and peripheral embolism. The risk of stroke in AF is heterogeneous and dependent on underlying clinical conditions included in current risk stratification schemes. Recently, the CHA2DS2-VASc score has been incorporated into guidelines to encompass common stroke risk factors observed in routine clinical practice. The aim of this study was to study the predictive value of CHA2DS2-VASc score on the prognosis of patients with AF to determine the correlation of major complications including cerebral infarction and intracranial hemorrhage in patients with AF with oral anticoagulant and antiplatelet aggregation drugs and to identify the risk factors for all-cause mortality. A prospective study was conducted on 181 patients with AF who underwent physical examinations at Hai'an Qutang Central Hospital from January 2020 to December 2020. The patient's general condition, chronic disease history, CHA2DS2-VASc [congestive heart failure, hypertension, age ≥75 years (doubled), diabetes, stroke (doubled), vascular disease, age 65 to 74 years, and sex category (female)] score, left ventricular ejection fraction (LVEF), lipid metabolism, and oral anticoagulant and antiplatelet aggregation medication during physical examination were recorded. By using telephone meetings to complete the follow-up, we tracked the patient's cerebral infarction, intracranial hemorrhage, and survival status within 2 years of follow-up, statistically analyzed the relationship between AF complications and medication, and grouped patients with AF based on the CHA2DS2-VASc score to evaluate its predictive ability for mortality outcomes in these patients. The patients were divided into four groups according to the medication situation, and the incidence of cerebral infarction in the combination group was significantly lower than that in the non-medication group (0.0% vs. 19.2%; P<0.01). The incidence of intracranial hemorrhage in the combination group was significantly higher than that in the non-drug group (13.8% vs. 0.0%; P<0.01). The logistic regression model indicated that patients with a history of cerebral infarction had an increased risk of death compared to those without a history of cerebral infarction [odds ratio (OR) =7.404; 95% confidence interval (CI): 2.255-24.309]. After grouping according to the CHA2DS2-VASc score, we found that there was a significant difference in the 2-year survival rate between patients with CHA2DS2-VASc score <5 and those with a score ≥5 (P<0.01). The characteristic curve analysis of the participants showed that the CHA2DS2-VASc score had good predictive ability for all-cause mortality in patients with AF (area under the curve =0.754), with a cutoff value of 4, a sensitivity of 62.50%, a specificity of 86.06%, and a 95% CI of 0.684-0.815. The CHA2DS2-VASc score demonstrated high predictive value for all-cause mortality in patients with AF.

Tissue Inhibitor of Metalloproteinases-2 (TIMP-2) as a Prognostic Biomarker in Acute Kidney Injury: A Narrative Review.

Acute kidney damage (AKI) is a serious and common consequence among critically unwell individuals. Traditional biomarkers, such as serum creatinine, frequently fail to detect AKI in its early stages, necessitating the development of new accurate early biomarkers. Tissue inhibitor of metalloproteinases 2 (TIMP-2) has emerged as a promising biomarker for predicting early AKI. The present narrative review investigates the role of TIMP-2 in AKI prediction in a variety of clinical scenarios. In the NephroCheck® test, TIMP-2 exceeds established biomarkers for the early identification of AKI in terms of sensitivity and specificity when combined with insulin-like growth factor-binding protein 7 (IGFBP-7). Elevated levels of these biomarkers can provide a warning signal for AKI two to three days before clinical symptoms appear. TIMP-2 and IGFBP-7 have high predictive values, with an area under the curve (AUC) typically above 0.8, indicating good predictive capacity. For example, the [TIMP-2] × [IGFBP-7] product produced an AUC of 0.85 in surgical patients at high risk. In critically ill patients, a threshold of 0.3 (ng/mL)2/1000 demonstrated 92% sensitivity and 72% specificity. Elevated TIMP-2 levels have been correlated with higher mortality rates and the need for renal replacement therapy (RRT). In sepsis-associated AKI (SA-AKI), TIMP-2 levels combined with clinical prognostic models improved predictive accuracy (AUC: 0.822). Furthermore, elevated urine TIMP-2 levels were good predictors of AKI in pediatric patients after cardiac surgery, with AUC-ROC values of up to 0.848. Urine output and the presence of concomitant disorders may influence the prognostic accuracy of these biomarkers; therefore, more research is needed to fully understand their utility. The predictive value of TIMP-2 could be strengthened by combining it with other clinical parameters, reinforcing its role in the early detection and treatment of AKI.

Prediction of acute coronary syndrome in patients with myeloproliferative neoplasms.

Patients with myeloproliferative neoplasms (MPN) are exposed to a higher risk of cardiovascular disease, especially cardiovascular calcification. The present research aimed to analyze the clinical features and coronary artery calcium score (CACS) in MPN patients, and construct an effective model to predict acute coronary syndrome (ACS) in MPN patients. A total of 175 MPN patients and 175 controls were recruited from the First Affiliated Hospital of Ningbo University. Based on cardiovascular events, the MPN patients were divided into the ACS group and the non-ACS group. Multivariate Cox analysis was completed to explore ACS-related factors. Furthermore, ROC curves were plotted to assess the predictive effect of CACS combined with white blood cells (WBC) and platelet for ACS in MPN patients. The MPN group exhibited a higher CACS than the control group (133 vs. 55, P < 0.001). A total of 16 patients developed ACS in 175 MPN patients. Compared with non-ACS groups, significant differences in age, diabetes, smoking history, WBC, percentage of neutrophil, percentage of lymphocyte, neutrophil count, hemoglobin, hematocrit, platelet, lactate dehydrogenase, β 2-microglobulin, and JAK2V617F mutation were observed in the ACS groups. In addition, the CACS in the ACS group was also significantly higher than that in the non-ACS group (374.5 vs. 121, P < 0.001). The multivariable Cox regression analysis identified WBC, platelet, and CACS as independent risk factors for ACS in MPN patients. Finally, ROC curves indicated that WBC, platelet, and CACS have a high predictive value for ACS in MPN patients (AUC = 0.890). CACS combined with WBC and platelet might be a promising model for predicting ACS occurrence in MPN patients.

Predictive value of interleukin-6 combined with serum neuron-specific enolase on the prognosis of acute ischemic stroke

ObjectiveTo explore the prognostic value of interleukin-6 (IL-6) combined with serum neuron specific enolase (NSE) in arterial atherosclerotic ischemic stroke. Methods116 patients with arterial atherosclerotic ischemic stroke admitted to the emergency ward of our Hospital were retrospectively analyzed. According to the score of modified Rankin scale (mRS) at 90 days after discharge, the patients were divided into the poor prognosis group (mRS > 2, n = 32) and the good prognosis group (mRS ≤ 2, n = 84). Activities of Daily Living (ADL) was used to evaluate the level of independence in activities of daily living after treatment. ResultsThe NIHSS score (14.91 ± 5.20 vs. 9.43 ± 4.30, P < 0.001), IL-6 (11.30 ± 3.11 vs. 6.75±1.28, P < 0.001) and NSE levels (12.47 ± 4.69 vs. 6.42 ± 1.32, P<0.001) in poor prognosis group were higher than those in the good prognosis group. At 90 days post-discharge, 100 % of the good prognosis group had ADL scores over 60, while in the poor prognosis group, 46.88 % scored 40–60, 40.63 % scored 20–40, 9.38 % scored under 20, and 3.13 % died. The AUC of NSE was 0.906 (95 % CI: 0.847–0.965, P < 0.001), the best cut-off value was 7.445 ng/mL, and the sensitivity and specificity were 75.0 % and 82.1 %, respectively. The AUC for IL-6 combined with NSE increased to 0.965 (95 %CI: 0.934–0.997, P < 0.001), and the sensitivity and specificity increased to 80.2 % and 92.9 %, respectively. ConclusionIL-6 ≥ 6.805 pg/mL and NSE ≥ 7.445 ng/mL were independently associated with poor prognosis in patients with AIS, and the combined testing of the two indicators had a higher predictive value. These results suggested that the combined assay of IL-6 and NSE could be a novel marker for predicting poor prognosis in AIS.

Long-term blood pressure variability and risk of cardiovascular diseases in populations with different blood pressure status: an ambispective cohort study.

We aimed to investigate the correlation between long-term blood pressure variability (BPV) and the risk of cardiovascular diseases (CVDs) among population with different blood pressure statuses (normotension, well-controlled hypertension, and uncontrolled hypertension). In this ambispective cohort study, CVD-free residents aged over 50 years were consecutively enrolled from two community health service centers (CHCs) in Tianjin, China from April 2017 to May 2017. Information on blood pressure was retrospectively extracted from electronic medical records of CHCs between January 2010 and May 2017, and the occurrence of new-onset CVDs was prospectively observed during follow-up until September 2019. Long-term variation of SBP and DBP was assessed using four indicators: SD, coefficient of variation (CV), average successive variability (ASV), and average real variability (ARV). Cox proportional hazards regression model was developed to identify the potential impact of BPV on the incidence of CVDs. The receiver operating characteristic curve (ROC) was utilized to evaluate the predictive value of BPV indicators for the occurrence of CVDs. Of 1275 participants included, 412 (32.3%) experienced new CVD events during the median 7.7 years of follow-up, with an incidence density of 499/10 000 person-year in the whole cohort. Cox regression analysis revealed that almost all SBP and DBP variability indicators (except for SBP-SD) were significantly related to the risk of CVDs, especially among individuals with well-controlled hypertension. A trend toward an increased risk of CVDs across BPV quartiles was also observed. Moderate predictive abilities of BPV were observed, with the area under ROC curves ranging between 0.649 and 0.736. For SBP variability, SD had the lowest predictive ability, whereas for DBP variability, ARV had the lowest predictive ability. No significant association of CVDs with SBP-SD was found in all analyses, no matter as a continuous or categorical variable. Elevated long-term BPV is associated with an increased risk of CVDs, especially among individuals with well-controlled hypertension. CV and ASV had higher predictive values than SD and ARV.

Predictive efficacy of the preoperative neutrophil–lymphocyte ratio in lymph node metastasis of cN0 hormone receptor-positive breast cancer

Breast cancer, as the most common cancer, has surpassed lung cancer worldwide. The neutrophil-to-lymphocyte ratio (NLR) has been linked to the onset of cancer and its prognosis in recent studies. However, quite a few studies have shown that there is a link between NLR and lymph node metastases in cN0 hormone receptor-positive (HR(+)) breast cancer. The purpose of this study was to evaluate the correlation between NLR and lymph node metastases in cN0 HR(+) breast cancer patients. From January 2012 to January 2022, 220 patients with cN0 HR(+) invasive breast cancers were enrolled in this study. The relationship between NLR and pathological data was statistically examined. The receiver operating characteristic (ROC) curve was used to determine the optimal cutoff of NLR, a chi-squared test was used for the univariate analysis, and logistic analysis was used for the multivariate analysis. The NLR had an optimal cutoff of 2.4 when the Jorden index was at a maximum. Patients with axillary lymph node metastases had a higher NLR (P < 0.05). A Univariate analysis showed that there were significant differences in cN0 HR(+) breast cancer with axillary lymph node metastasis among different clinical stages, histological grades, Ki-67 levels, tumor sizes, and NLR levels (P < 0.05). Clinical stage, tumor size, and NLR were found to be independent risk factors for lymph node metastases in multifactorial analysis. In cN0 HR(+) breast cancer, NLR is an independent risk factor for lymph node metastases. An NLR ≥ 2.4 indicates an increased probability of lymph node metastases. An elevated preoperative NLR has a high predictive value for axillary lymph node metastases.

The Factors for the Occurrence of Pulmonary Infection after Gastrointestinal Surgery and the Construction of a Predictive Model Using sTREM-1 and TIM-4: A Retrospective Study.

Identifying and intervening with high-risk postoperative pulmonary infections patients pose challenges in clinical practice. This study aims to conduct a comprehensively analysis of the risk factors and predictive factors associated with post-gastrointestinal surgery pulmonary infections and to develop a predictive model that can predict occurrence of pulmonary infection. A retrospective analysis was conducted on 96 patients who underwent gastrointestinal surgery at our hospital from May 2021 to October 2023. The occurrence rate of postoperative pulmonary infections was calculated, and patients were categorized into two groups: those with pulmonary infections (the occurrence group) and those without pulmonary infections (the non-occurrence group). Logistic regression analysis was utilized to identify the risk factors for post-gastrointestinal surgery pulmonary infections and to evaluate the predictive value of soluble triggering receptor expressed on myeloid cells-1 (sTREM-1) and T cell immunoglobulin and mucin domain-4 (TIM-4) using nomograms, calibration curves, and Receiver Operating Characteristic (ROC) curves. Out of 96 patients, 20 (20.83%) developed postoperative pulmonary infections. Significant differences were noted between occurrence and non-occurrence groups in terms of smoking (65.00% vs. 34.21%, p = 0.013), surgical duration (70.00% vs. 31.58%, p = 0.002), Preoperative hemoglobin level (35.00% vs. 65.79%, p = 0.013), sTREM-1 levels (23.57 ± 3.16 pg/mL vs. 15.62 ± 2.48 pg/mL, p < 0.001), and TIM-4 levels (61.48 ± 6.35 pg/mL vs. 44.73 ± 5.22 pg/mL, p < 0.001). Logistic regression analysis leads to the development of a risk prediction model for post-gastrointestinal surgery pulmonary infections. The high predictive values of sTREM-1 (Area Under Curve (AUC) = 0.962, 95% confidence interval (CI) 0.917~0.999) and TIM-4 (AUC = 0.970, 95% CI 0.925~1.000) were highlighted by the AUC values, underscoring their clinical importance. A predictive model utilizing sTREM-1 and TIM-4 for pulmonary infection following gastrointestinal surgery was developed. Additionally, other risk factors such as smoking, surgical duration, and preoperative hemoglobin level were evaluated. This finding can be applied in clinical practice to identify potentially susceptible patients and facilitate early intervention measures.

Identification of Alcohol Use Prior to Major Cancer Surgery: Timeline Follow Back Interview Compared to Four Other Markers

Background: The postoperative complication rate is 30–64% among patients undergoing muscle-invasive and recurrent high-risk non-muscle-invasive bladder cancer surgery. Preoperative risky alcohol use increases the risk. The aim was to evaluate the accuracy of markers for identifying preoperative risky alcohol. Methods: Diagnostic test sub-study of a randomized controlled trial (STOP-OP trial), based on a cohort of 94 patients scheduled for major bladder cancer surgery. Identification of risky alcohol use using Timeline Follow Back interviews (TLFB) were compared to the AUDIT–C questionnaire and three biomarkers: carbohydrate-deficient transferrin in plasma (P–CDT), phosphatidyl-ethanol in blood (B–PEth), and ethyl glucuronide in urine (U–EtG). Results: The correlation between TLFB and AUDIT–C was strong (ρ = 0.75), while it was moderate between TLFB and the biomarkers (ρ = 0.55–0.65). Overall, sensitivity ranged from 56 to 82% and specificity from 38 to 100%. B–PEth showed the lowest sensitivity at 56%, but the highest specificity of 100%. All tests had high positive predictive values (79–100%), but low negative predictive values (42–55%). Conclusions: Despite high positive predictive values, negative predictive values were weak compared to TLFB. For now, TLFB interviews seem preferable for preoperative identification of risky alcohol use.

Predicting Dental General Anesthesia Use among Children with Behavioral Health Conditions.

To evaluate how different data sources affect the performance of machine learning algorithms that predict dental general anesthesia use among children with behavioral health conditions. Observational study using claims data. Using Medicaid claims from Partners For Kids (2013-2019), electronic medical record data, and the Ohio Child Opportunity Index, we conducted a retrospective cohort study of 12,410 children with behavioral health diagnoses. Four lasso-regularized logistic regression models were developed to predict dental general anesthesia use, each incorporating different data sources. Lift scores, or the ratio of positive predictive value to base case prevalence, were used to compare models, and a lift score of 2.5 was considered minimally acceptable for risk prediction. Dental general anesthesia use ranged from 3.2% to 3.9% across models, which made it difficult for the machine learning models to achieve high positive predictive value. Model performance was best when either the electronic medical record (lift = 2.59) or Ohio Child Opportunity Index (lift = 2.56), but not both (lift = 2.34) or neither (lift = 1.87), was used. Incorporating additional data sources improved machine learning model performance, and 2 models achieved satisfactory performance. The model using electronic medical record data could be applied in hospital-based settings, and the model using the Ohio Child Opportunity Index could be more valuable in community-based settings. Machine learning was applied to satisfactorily predict which children with behavioral health diagnoses would require dental treatment under general anesthesia. Incorporating electronic medical record data or area-level social determinants of health data, but not both, improved the performance of the machine learning predictions. The 2 highest performing models could be applied by hospitals using medical record data or by organizations using area-level social determinants of health data to risk stratify the pediatric behavioral health population.

Correlation and consistency between two detection methods for serum 25 hydroxyvitamin D levels in human venous blood and capillary blood.

The study assessed the correlation and concordance of 25-hydroxyvitamin D [25(OH)D] levels in capillary and venous plasma collected simultaneously after vitamin D3 supplementation in 42 healthy adults. They were randomly divided into three groups by random number table method. Group A took 1,000 IU vitamin D3 daily, group B took 10,000 IU vitamin D3 every 10 days, and group C took 30,000 IU vitamin D3 every 30 days until the end of the 12th month. Venous blood serum 25(OH)D level was detected by chemiluminescence immunoassay (CLIA) and mass spectrometry (LC-MS) at day 1, day 14, day 28, month 6, and month 12 respectively, the capillary blood serum 25(OH)D level was detected by chemiluminescence immunoassay (CLIA) at the same time. Pearson correlation analysis and linear regression analysis were employed to investigate the relationship and transformation equation between the findings of the two samples and the results obtained from different detection methods within the same sample. The Bland-Altman method, Kappa analysis, and receiver operating characteristic (ROC) curve were utilized for assessing consistency, sensitivity, and specificity. The three groups all reached a stable peak at 6 months, and the average levels of the three groups were 49.21, 42.50 and 43.025 nmol/L, respectively. The average levels of group A were higher than those of group B and group C (P < 0.001). The mean values of serum 25(OH)D measured by LC-MS and CLIA in 42 healthy adults were 45.32 nmol/L and 49.88 nmol/L, respectively, and the mean values of 25(OH)D measured by LC-MS in capillary blood were 52.03 nmol/L, and the difference was statistically significant (P < 0.001). Pearson correlation analysis showed that the linear fitting formula of scatter data was as follows: venous 25(OH)D concentration (nmol/L) = 1.105 * capillary 25(OH)D concentration -7.532 nmol/L, R2 = 0.625. Good agreement was observed between venous and corrected capillary 25(OH)D levels in clinical diagnosis (Kappa value 0.75). The adjusted serum 25(OH)D in capillary blood had a high clinical predictive value. The agreement between the two methods is good when the measured 25(OH)D level is higher. Standardized capillary blood chemiluminescence method can be used for 25(OH)D detection.

Predictive Value of Serum Inflammatory Factors and FT3 for Stroke-Associated Pneumonia in Patients With Acute Ischemic Stroke

Objective: The ability of serum inflammatory factors and free triiodothyronine (FT3) in predicting the occurrence of stroke-associated pneumonia (SAP) in patients with acute ischemic stroke (AIS) was assessed in this study. Methods: A retrospective analysis was conducted on 285 consecutive patients with AIS initially diagnosed and admitted to our hospital from January to December 2022. Patients were categorized into SAP and non-SAP groups based on the presence of SAP. Both groups were compared in terms of baseline characteristics, including National Institute of Health Stroke Scale (NIHSS) score, SAP risk assessment (A2DS2), TOAST classification. Independent risk factors for SAP were identified using multivariate logistic regression analysis, and the predictive value of inflammatory markers was evaluated through ROC curves. Results: Among 285 patients with AIS, 40 (14.03%) were found to have developed SAP. Higher NIHSS and A2DS2 scores, elevated serum IL-1β, IL-8, and IL-33 levels, increased age, atrial fibrillation, swallowing difficulties, and a higher proportion of patients with low FT3 levels were observed in the SAP group compared with the non-SAP group (all P&lt;0.05). Significant risk factors for SAP in patients with AIS were identified through multivariate logistic regression analysis, including age, swallowing difficulties, NIHSS, A2DS2 , IL-1β , IL-8 , IL-33, and FT3 (P&lt;0.05). The highest predictive values were observed for A2DS2, FT3, and IL-8 with AUC values of 0.854, 0.844, and 0.823, respectively. Conclusion: SAP can be highly predicted by A2DS2, FT3, and IL-8, enabling the early identification of patients with high-risk SAP and facilitating timely intervention and treatment.

Spot urine sodium-to-creatinine ratio surpasses sodium in identifying poor diuretic response in acute heart failure.

We aim to identify the most accurate marker for early prediction of poor diuretic response in acute heart failure (AHF) patients with signs of congestion requiring intravenous diuretic treatment. In this single-centre, prospective observational study, AHF patients with signs of congestion received a standardized intravenous furosemide dose (1mg/kg of body weight; 40mg in bolus and remaining dose in 2h continuous infusion). Subsequently, we assessed spot urine composition at 2h post-administration, comparing it with total urine output at 6h. Various potential urine markers were analysed for predicting urine output using receiver operating characteristic (ROC) curves and logistic regression models. We investigated guideline-recommended markers, including spot urine sodium (UNa+) and its cut-off, and introduced the UNa+/UCr (urine creatinine concentration) ratio adjusting UNa+ for urine dilution. Out of 111 patients (85% males, 66.4±13.9years old, NTproBNP 7290 [4493-14582] pg/ml), there were 18 (16%) with a poor diuretic response (cumulative urine output <600ml during the first 6h). The mean 6h cumulative diuresis in patients with poor and good diuretic response was 406±142 and 2114±1164ml, respectively, P<0.005. After an initial evaluation of several potential biomarkers, only UNa+, UCr and UNa+/UCr were selected as candidates with the highest predictive value. The cut-off for UNa+ adjusted for urine dilution: UNa+/UCr ratio <0.167mmol/mg×10-1 was determined by ROC analysis with the highest area under the curve (95% confidence interval): 0.956 (0.915-0.997), P<0.001. When compared with the guideline-recommended cut-off (UNa+ <50mmol/L as a reference, specificity-0.97; sensitivity-0.83), the odds ratio (OR) for UNa+/UCreat to identify a poor diuretic response was 2.5 times greater, regardless of kidney function (OR for estimated glomerular filtration rate in the logistic regression model was 0.978 [0.945-1.013, P=0.222]). The UNa+/UCr ratio in a spot urine sample 2h after intravenous diuretic administration is a simple, highly predictive marker for the identification of AHF patients with poor diuretic response, surpassing guidelines-recommended markers like UNa+.

Prediction and explanation of debris flow velocity based on multi-strategy fusion Stacking ensemble learning model

The debris flow velocity fundamentally determines its intensity, thereby rendering its prediction a crucial aspect of disaster prevention and control strategies. However, accurate velocity prediction has consistently posed significant challenges due to the intricate interplay of various influential factors. To address the limitations of existing models, an explainable multi-strategy fusion of Stacking ensemble learning is proposed. Initially, an improved snake optimization (ISO) algorithm is employed to adjust parameters within the model’s learners. Benchmark function comparison tests are then conducted to validate the reliability of the ISO algorithm. Subsequently, a learner selection method based on predictive performance and degree of difference is established to facilitate the selection of basic learners and meta-learners. This leads to the construction of the Stacking ensemble learning model, achieved through the integration of parameter optimization strategies from the improved swarm intelligence algorithm strategy, the error weighting strategy, and the decomposition strategy. To assess the model, a case study of the Jiangjiagou Gulley debris flow is undertaken, focusing on the prediction of the debris flow velocity. The results demonstrate high predictive accuracy, with RMSE, MAE, and MAPE values of 0.19, 0.17, and 2.46% respectively. Furthermore, under the SHAP framework, global and local explanations of the predictions are provided. Through feature importance analysis, the bed slope gradient is identified as the most crucial feature in the velocity prediction of the Jiangjiagou Gulley debris flow. Coupling effects and contributions of input features to the debris flow velocity prediction are further analyzed and explained through feature interaction analysis and single sample analysis. This study not only provides a new method for debris flow velocity prediction but also provides guiding suggestions for debris flow monitoring and control.

A COMPARATIVE STUDY OF ULTRASOUND AND COMPUTED TOMOGRAPHY IN EVALUATION OF ACUTE AND CHRONIC PANCREATITIS, ASSOCIATED COMPLICATIONS AND PREDICTING SEVERITY AND PROGNOSIS

Objectives: (1) The objective of the study was to determine the value of USG and computed tomography (CT) in early diagnosis of acute pancreatitis (AP). (2) The study aimed to compare which of the above two radiological investigations (USG/CT) provides the most important information of the disease process and to determine value of CT in evaluating complications, morbidity/severity, and predicting prognosis of AP using modified CT severity index (MCTSI). Methods: This study included 50 patients diagnosed with pancreatitis, consisting of 38 cases of AP and 12 cases of chronic pancreatitis (CP). Both USG and contrast-enhanced CT (CECT) were used to visualize the pancreas, assess its size, detect peripancreatic inflammation and fluid collections, and identify the extent of necrosis and complications. The sensitivity and specificity of USG and CECT were compared. The severity of AP was classified using the MCTSI within 3 days of symptom onset. Clinical outcomes, including hospital stay duration, organ failure, systemic infection, and the need for surgical intervention, were recorded and correlated with MCTSI scores. Statistical Package for the Social Sciences 22.0 was used for statistical analysis and p&lt;0.05 was taken as statistically significant. Results: The mean age of patients was 37.92±12.14 years. USG had a sensitivity of 58% for detecting AP, significantly lower than the 95% sensitivity of CECT, primarily due to bowel gas interference. Both USG and CECT had high positive predictive values. The MCTSI effectively classified the severity of AP, with 41.5% of cases categorized as mild, 39% as moderate, and 19.5% as severe. Extrapancreatic complications were significantly correlated with adverse clinical outcomes and end-organ failure when included in the MCTSI scoring. USG was adequate for diagnosing CP through visualization of dilated ducts, calcifications, and atrophic pancreas, but CECT demonstrated higher specificity and accuracy, especially for rare forms like groove and mass-forming pancreatitis. The study showed a strong correlation between MCTSI scores and patient outcomes. The mortality rate was 2%, observed only in patients with severe AP. Conclusion: The MCTSI is a valuable tool for accurately classifying the severity of AP and predicting clinical outcomes. CECT is superior to USG in diagnosing and managing pancreatitis, providing better visualization and assessment of complications. While USG is useful for diagnosing CP, CECT offers greater specificity and accuracy. The study supports the use of MCTSI in routine clinical practice to guide the management and predict outcomes in patients with AP.