Short-term Predictor Research Articles

Clinical significance of CA 19-9 elevation during postoperative surveillance for extrahepatic bile duct cancer: a nomogram-based approach for the prediction of short-term recurrence

BackgroundThis study aimed to assess the significance of elevated carbohydrate antigen (CA) 19-9 in postoperative surveillance of extrahepatic bile duct cancer and to identify short-term recurrence predictors. MethodsThis retrospective study included patients with elevated CA 19-9 post-curative surgery. Patients were categorized into positive and negative CT groups based on the detection of recurrence at CA 19-9 elevation. Short-term recurrence was defined as recurrence within 6 months in the negative CT group. We identified the factors associated with short-term recurrence and devised a predictive nomogram. ResultsAmong the 190 patients, 91 (47.9%) exhibited tumor recurrence with CA 19-9 elevation (CT-positive group), whereas 99 (52.1%) showed no recurrence (CT-negative group). In the CT-negative group (n=99), 22 (22.2%) experienced short-term tumor recurrence within 6 months. Preoperative CA 19-9 (odds ratio [OR]: 1.5, p=0.016), postoperative CA 19-9 (OR: 1.9, p=0.047), adjuvant treatment (OR: 3.5, p=0.032), and the absence of inflammation (OR: 3.5, p=0.045) were predictors of short-term recurrence. The area under the curve of the nomogram was 0.80 (95% CI: 0.69–0.90). DiscussionDespite elevated CA 19-9 levels, approximately 50% of patients exhibited no recurrence during postoperative surveillance for extrahepatic bile duct cancer. Factors influencing short-term recurrence encompass pre- and postoperative CA 19-9, adjuvant treatment, and inflammatory status.

Convolutional Long Short-Term Memory Predictor for Collaborative Remotely Operated Vehicle Trajectory Tracking in a Leader–Follower Formation Subject to Communication and Sensor Latency in the Presence of External Disturbances

Nowadays, collaborative operations between Remotely Operated Vehicles (ROVs) face considerable challenges, particularly in leader–follower schemes. The underwater environment imposes limitations on acoustic modems, leading to reduced transmission speeds and increased latency in ROV position and speed transmission. This complicates effective communication between the ROVs. Traditional methods, such as Recursive Least Squares (RLS) predictors and the Kalman filter, have been employed to address these issues. However, these approaches have limitations in handling non-linear patterns and disturbances in underwater environments. This paper introduces a Convolutional Long Short-Term Memory (ConvLSTM) predictor designed to enhance communication and trajectory tracking between ROVs in a leader–follower scheme. The proposed ConvLSTM aims to address the shortcomings of previous methods by adapting effectively to varying conditions, including ocean currents, communication delays, and signal interruptions. Simulations were conducted to evaluate ConvLSTM’s performance and compare it with other advanced predictors, such as Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM), under different conditions. The results demonstrated that ConvLSTM achieved a 13.9% improvement in trajectory tracking, surpassing other predictors in scenarios that replicate real underwater conditions and multi-vehicle communication. These results highlight ConvLSTM’s potential to significantly enhance the performance and stability of collaborative ROV operations in dynamic underwater environments.

The short-term outcomes and risk factors of post-myocardial infarction ventricular septal rupture: a multi-center retrospective Study

ObjectivesPost-myocardial infarction ventricular septal rupture (PIVSR) is one of the most severe types of mechanical complications after acute myocardial infarction (AMI) with high mortality and poor prognosis. The risk factors for short-term mortality of patients with PIVSR may be not widely recognized. We aimed to assess the prevalence and short-term mortality risk predictors of PIVSR.MethodsA total of 62 patients with a diagnosis of PIVSR were admitted to three top general public hospitals in Chongqing, China. Clinical characteristics and short-term outcomes of patients with PIVSR were compared. Predictors of PIVSR were assessed using logistic regression analysis.ResultsMean age was 70.7 ± 10.7 years (38.7% female). The overall in-hospital mortality of PIVSR remained high (71%). Most (47/62) of the patients were in Killip class III or IV at the time of rupture diagnosis. Logistic regression analysis revealed that white blood cell count (WBC, OR 1.619, 95% CI 1.172–2.237, P = 0.005), cardiogenic shock (OR 47.706, 95%CI 2.859-795.945, P = 0.007) and left ventricular ejection fraction (LVEF, OR 0.803, 95%CI 0.689–0.936, P = 0.009) were independent risk factors of in-hospital early mortality. The nomogram developed for predicting the risk of short-term mortality showed a robust discrimination, with an area under the receiver operating characteristic curve (AUC) of 0.956 (95%CI 0.912-1.000).ConclusionThe short-term mortality of PIVSR remained high. WBC, cardiogenic shock, and LVEF were the independent predictive factors of short-term mortality. Our nomogram might be used to predict early mortality of patients with PIVSR.

Electroencephalographic outcomes and predictors of epilepsy in autoimmune encephalitis

BackgroundAn abnormal EEG is pivotal in diagnosis, exclusion of mimickers and prognosticating epilepsy in Autoimmune Encephalitis (AE). However, little is known about the short and long term electroencephalographic outcomes and predictors of epilepsy in AE. This study aims to describe the seizure characteristics and electrophysiological markers of various AE subtypes and assess the clinical and electrophysiological predictors of autoimmune epilepsy. MethodsClinical features and EEGs in 74 patients (acute phase=39 and post-acute phase defined after a minimum eight weeks after acute phase=35) of AE fulfilling the proposed criteria were reviewed in their respective acute phases and at six months follow-up. ResultsThe mean age of presentation (N = 74, 45 females) was 21.8 (21.8 ± 17.0) years. 38 (51 %) patients were <18 years. Seizures were present in 55 (74 %) patients with poor response to ASMs (p = 0.039). 39 (52.7 %) EEGs were abnormal in acute phase. Anti-NMDAR AE had most frequently abnormal EEG (63.4 %). Poor background reactivity and theta range slowing were most common abnormalities. FIRDA, EDB and delta range slowing were seen in seropositive AE (P = 0.003). Mutism, psychiatric features and incontinence correlated with abnormal EEG (p = 0.013, p = 0.028 and p = 0.025). Background slowing and epileptiform discharges predicted worse cognitive scores at follow-up (p = 0.012). Eight (11.9 %) patients developed epilepsy. Status epilepticus at presentation (p = 0.009), seronegative status (p = 0.0020), delayed initiation of immunotherapy (p = 0.012), abnormal MRI (p = 0.003) and abnormal EEG (p = 0.004) at onset indicate development of autoimmune epilepsy ConclusionsFIRDA, EDB and delta range slowing with refractory seizures suggest AE. Epileptiform abnormalities, status epilepticus and seronegativity predict autoimmune epilepsy.

A multiclass short-term lightning predictor for the Amazon region using ground-based meteorological data and artificial neural network

Lightning prediction is a complex task due to the intricate nature of this phenomenon. Various sectors of society, including aviation and civil defense, can benefit from accurate lightning occurrence information. Methodologies that can predict lightning occurrence are crucial to minimize the issues related to this phenomenon. In this work, we propose a novel approach using the machine learning model called Artificial Neural Network (ANN) to predict lightning occurrence one hour ahead. We use ground-based meteorological data from the National Institute of Meteorology (INMET) and the number of lightning per hour to feed the ANN model. We used a lightning dataset of six years of observation (2015–2020) from GLD360 to train, validate, and test machine learning algorithm models. A model was developed for each weather station. We use data from 24 stations distributed throughout the Brazilian Legal Amazon region. The multiclass predictor was based on three categories: no lightning, low-intensity, and high-intensity of lightning. We also developed a solution comprising two cascade ANN models, each performing binary prediction. The first predicts the occurrence or no occurrence of lightning, and the second predicts the intensity: low or high. The cascade approach showed superior performance compared to the regular approach.

Abstract 1070: Predicting Short Term Mortality In Patients With Acute Pulmonary Embolism With Deep Learning

Background: Pulmonary embolism (PE) is one of the leading causes of short-term mortality among cardiovascular diseases. Rapid and accurate stratification of PE-related risk is vital for effective patient management. A strong potential research in this direction is to utilize deep learning (DL) approaches. DL approaches have enhanced risk prediction performance by leveraging novel risk predictors and analyzing complex interrelationships among them. In this study, we developed a DL based new scoring system superior to the current standard, the Pulmonary Embolism Severity Index (PESI) , when predicting the short-term (0-30 days) mortality of PE. Materials and Methods: Our study included a total of 207 patients. The diagnosis of acute PE was confirmed using contrast-enhanced computed tomography (CT) scans. DL model was created using CT scans and corresponding ground truth labels (short term mortality). We have benchmarked different DL architectures for this purpose including CNN and Transformers. Results: Out of 207 patients, 53 of them died during short term . The research group was categorized into two groups based on their short term survival status. Multivariable logistic regression analysis was used to determine the independent relationships between variables and short term mortality. Short-term mortality was predicted statistically significantly (p&lt;0.001) in the DL model developed from the patient's CT images (Figure 1). Then, we developed a new scoring model by uniquely combining the short-term mortality independent predictors, baseline data and the DL model (Figure 1). The ROC analysis demonstrated an AUC of 0.976 (p&lt;0.001) for the Baseline-DL model and an AUC of 0.865 (p&lt;0.001) for the PESI score The Baseline-DL model was statistically significantly superior to the PESI (p&lt;0.001). Conclusion: We developed a DL based new scoring system to predict short term mortality of patients with PE, significantly superior to the PESI.

Hematocrit Predicts Poor Prognosis in Patients with Acute Ischemic Stroke or Transient Ischemic Attack.

This study aims to investigate the association between HCT (Hematocrit) levels and adverse outcomes in patients with acute ischemic stroke (AIS) or transient ischemic attack (TIA); 14,832 participants from the China National Stroke Registry-III with AIS or TIA were analyzed. Participants were categorized into quartiles based on baseline HCT levels. The primary outcome was poor functional outcomes (modified Rankin Scale ≥ 3) during three months, with secondary outcomes including all-cause death, stroke recurrence, and combined vascular events. Logistic regression or Cox regression models were used to assess the relationship between HCT and clinical outcomes. Compared to the third quartile, patients in the lowest quartile group showed increased risk of poor functional outcome (adjusted OR: 1.35, 95% CI: 1.15-1.58, p < 0.001), patients in the lowest quartile had a higher risk of all-cause death (adjusted HR: 1.68, 95% CI: 1.06-2.68, p = 0.028), as did those in the highest quartile (adjusted HR: 2.02, 95% CI: 1.26-3.25, p = 0.004). Sensitivity analysis shows that the association of HCT with all-cause death weakened, while the association with poor functional outcome was strengthened after excluding patients with recurrent stroke. Our results indicated that HCT level could be used as a short-term predictor for poor functional outcomes and all-cause death in patients with AIS or TIA.

Short-term predictors of stereotactic radiosurgery outcome for untreated single non-small cell lung cancer brain metastases: a restrospective cohort study.

Stereotactic radiosurgery (SRS) is an option for brain metastases (BM) not eligible for surgical resection, however, predictors of SRS outcomes are poorly known. The aim of this study is to investigate predictors of SRS outcome in patients with BM secondary to non-small cell lung cancer (NSCLC). The secondary objective is to analyze the value of volumetric criteria in identifying BM progression. This retrospective cohort study included patients >18 years of age with a single untreated BM secondary to NSCLC. Demographic, clinical, and radiological data were assessed. The primary outcome was treatment failure, defined as a BM volumetric increase 12 months after SRS. The unidimensional measurement of the BM at follow-up was also assessed. One hundred thirty-five patients were included, with a median BM volume at baseline of 1.1 cm3 (IQR 0.4-2.3). Fifty-two (38.5%) patients had SRS failure at follow-up. Only right BM laterality was associated with SRS failure (p=0.039). Using the volumetric definition of SRS failure, the unidimensional criteria demonstrated a sensibility of 60.78% (46.11%-74.16%), specificity of 89.02% (80.18%-94.86%), positive LR of 5.54 (2.88-10.66) and negative LR of 0.44 (0.31-0.63). SRS demonstrated a 61.5% local control rate 12 months after treatment. Among the potential predictors of treatment outcome analyzed, only the right BM laterality had a significant association with SRS failure. The volumetric criteria were able to identify more subtle signs of BM increase than the unidimensional criteria, which may allow earlier diagnosis of disease progression and use of appropriate therapies.

Clinical and Laboratory Predictors of Long-Term Outcomes after Catheter Ablation for a Ventricular Electrical Storm.

Ventricular electrical storm (VES) is characterized by the occurrence of multiple episodes of sustained ventricular arrhythmias (VA) over a short period of time. Radiofrequency ablation (RFA) has been reported as an effective treatment in patients with ventricular tachycardia (VT). The aim of the present study was to indicate the short-term and long-term predictors of recurrent VA after RFA was performed due to VES. A retrospective, single-centre study included patients, who had undergone RFA due to VT between 2012 and 2021. In terms of the short-term (at the end of RFA) effectiveness of RFA, the following scenarios were distinguished: complete success: inability to induce any VT; partial success: absence of clinical VT; failure: inducible clinical VT. In terms of the long-term (12 months) effectiveness of RFA, the following scenarios were distinguished: effective ablation: no recurrence of any VT; partially successful ablation: VT recurrence; ineffective ablation: VES recurrence. The study included 62 patients. Complete short-term RFA success was obtained in 77.4% of patients. The estimated cumulative VT-free survival and VES-free survival were, respectively, 28% and 33% at the 12-month follow-up. Ischemic cardiomyopathy and complete short-term RFA success were predictors of long-term RFA efficacy. Neutrophil to lymphocyte ratio (NLR) and GFR <60 mL/min/1.73 m2 were associated with VES recurrence. NLR ≥2.95 predicted VT and/or VES recurrence with a sensitivity of 66.7% and specificity of 72.2%. Ischemic cardiomyopathy and short-term complete success of RFA were predictors of no VES recurrence during the 12-month follow-up, while NLR and GFR <60 ml/min/1.73 m2 were associated with VES relapse.

Daily prediction of inpatient suicide attempts using routinely collected theory-driven data.

We lack knowledge about the short-term predictors of suicide attempts (SAs) among treatment-seeking individuals. The current study evaluated whether (a) interpersonal difficulties, hopelessness, and affective states are associated with an increased risk of SAs on the same and the next day; (b) these daily states are interconnected differently over time among inpatients who attempt suicide compared to those who do not. In total, 110 psychiatric inpatients who attempted suicide during their stay at a psychiatric hospital self-reported their suicidal ideation, negative affect, positive affect, wish to live, interpersonal needs, and hopelessness each day (3,018 daily reports). Multilevel structural equation modeling was used to examine same-day and next-day predictors of SAs. Multilevel temporal network models assessed interconnectedness between daily predictors and were compared to network models from a matched sample of 110 psychiatric inpatients who did not attempt suicide. In multivariate models, increases in perceived burdensomeness were significantly associated with same-day SAs, whereas increased hopelessness was associated with next-day SAs. Network models for patients who attempted suicide indicated that hopelessness and suicidal ideation were central to change, leading to next-day deteriorations in mental health. In subsequent models, feeling calm and relaxed, and feeling fresh and rested were centrally connected to other variables. The centrality of these metrics tended to be higher than in the network models for patients who did not attempt suicide, suggesting differences in the interplay between risk and protective factors. This study suggests routinely monitoring interpersonal factors and hopelessness may help identify increased short-term risk of SAs among psychiatric inpatients. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

A comparative study of two X2.2 and X9.3 solar flares observed with HARPS-N

Context. Stellar flares cannot be spatially resolved, which complicates ascertaining the physical processes behind particular spectral signatures. Due to their proximity to Earth, solar flares can serve as a stepping stone for understanding their stellar counterparts, especially when using a Sun-as-a-star instrument and in combination with spatially resolved observations. Aims. We aim to understand the disk-integrated spectral behaviors of a confined X2.2 flare and its eruptive X9.3 successor, which had energies of 2.2 × 1031 erg and 9.3 × 1031 erg, respectively, as measured by Sun-as-a-star observations with the High Accuracy Radial velocity Planet Searcher for the Northern hemisphere (HARPS-N). Methods. The behavior of multiple photospheric (Na D1 &amp; D2, Mg I at 5173 Å, Fe I at 6173 Å, and Mn I at 4031 Å) and chromospheric (Ca II H &amp; K, Hα, Hβ, and He ID3) spectral lines were investigated by means of activity indices and contrast profiles. A number of different photospheric lines were also investigated by means of equivalent widths, and radial velocity measures, which were then related to physical processes directly observed in high-resolution observations made with the Swedish 1-m Solar Telescope (SST) and the Atmospheric Imaging Assembly (AIA) on board of the Solar Dynamics Observatory (SDO). Results. Our findings suggest a relationship between the evolving shapes of contrast profile time and the flare locations, which assists in constraining flare locations in disk-integrated observations. In addition, an upward bias was found in flare statistics based on activity indices derived from the Ca II H &amp; K lines. In this case, much smaller flares cause a similar increase in the activity index as that produced by larger flares. Hα-based activity indices do not show this bias and are therefore less susceptible to activity jitter. Sodium line profiles show a strongly asymmetric response during flare activity, which is best captured with a newly defined asymmetrical sodium activity index. A strong flare response was detected in Mn I line profiles, which is unexpected and calls for further exploration. Intensity increases in Hα, Hβ, and certain spectral windows of AIA before the flare onset suggest their potential use as short-term flare predictors.

Short-Term Prognosis of Elderly Patients Admitted to the Coronary Care Unit: A Subgroup Analysis of the MORCOR-TURK (Mortality and Morbidity in Coronary Care Units in Türkiye) Trial.

This subgroup analysis of the MORCOR-TURK (Mortality and Morbidity in Coronary Care Units in Türkiye) trial aimed to determine the short-term prognosis, mortality rates, and predictors for elderly patients followed in coronary care units (CCUs) in Türkiye. The MORCOR-TURK trial is a national, non-interventional, multicenter observational study conducted in Türkiye (ClinicalTrials.gov number NCT05296694). The study population includes CCU patients from 50 centers selected from all regions of Türkiye (between September 1 and 30, 2022 prospectively). In the subgroup analysis, patients were divided into two groups: Group 1 (ages 65 to < 75 years, n = 923 patients) and Group 2 (ages ≥ 75 years, n = 713 patients). At the end of the analysis, short-term prognosis, mortality rates, and predictors were documented. The mean age of Group 1 was 69 (67-72) years, and Group 2 was 80 (77-84) years. Chest pain was the most common reason for admission (968 patients [59.16%]), and acute coronary syndrome was the most common reason for hospitalization in the CCU (1,053 patients [64%]). Atrial fibrillation (AF) was the most common arrhythmia (356 patients [21.76%]). The mortality rate was 6.11% in elderly patients (4.23% in Group 1 and 8.56% in Group 2). The multivariate regression analysis showed that age (P = 0.046, P = 0.003), chronic kidney disease (P = 0.011, P = 0.045), and ventricular tachycardia/ventricular fibrillation (VT/VF) during hospitalization (P < 0.001) were the main factors that increased mortality in both groups. Other independent mortality risk factors were smoking for Group 1 and aortic stenosis for Group 2. This study represents the most comprehensive assessment of the short-term prognosis for elderly patients admitted to CCUs in Türkiye. It showed that coronary artery disease was the most common reason for admission and age over 75 and chronic kidney disease were the main determinants of mortality.

Impact of thrombocytopenia on short-term outcomes in patients undergoing mobile extracorporeal membrane oxygenation support.

The prolonged use of extracorporeal membrane oxygenation (ECMO) support is associated with increased consumption of platelets and hemolysis. The prognostic impact of thrombocytopenia prior to and during ECMO support on patient's short-, mid- and long-term outcomes has been critically evaluated and discussed over the last years. However, only few data have been published on thrombocytopenia caused by mobile ECMO support. The aim of this study was to evaluate the impact of thrombocytopenia on short-term outcomes and predictors of in-hospital mortality in patients supported by mobile ECMO for transportation and subsequent weaning in a tertiary centre. This retrospective single-centre study analyzed a total of 117 patients requiring mobile veno-arterial (va) ECMO support and subsequent transportation from referral hospitals to our department from January 2015 until December 2021. A total of 15 patients had to be excluded from the analysis for missing data regarding baseline platelet count. Patients were divided into two groups: thrombocytopenia group (<130 × 109/L, n = 44) and non-thrombocytopenia group (≥130 × 109/L, n = 58). The primary outcome was in-hospital mortality. Secondary outcomes were successful ECMO-weaning, and the incidence of associated complications (bleeding, acute hepatic failure, acute renal failure, dialysis, and septic shock). The dialysis rate before ECMO initiation was significantly higher (p = .041) in the thrombocytopenia group compared to the non-thrombocytopenia group. The rates of bleeding complications (p = .032) and limb ischemia (p = .003) were significantly higher in patients with low platelet count. Moreover, complication rates of acute hepatic failure (p < .001), acute renal failure (p < .001) and dialysis (p = .033) were significantly higher in the thrombocytopenia group. Also, in-hospital mortality was significantly higher (p = .002) in patients with low platelet count before initiation of ECMO support. Based on the results of the present study, patients with thrombocytopenia prior to mobile vaECMO support may be at significantly higher risk for associated complications and short-term mortality.

Short-term predictor for COVID-19 severity from a longitudinal multi-omics study for practical application in intensive care units

BackgroundThe COVID-19 pandemic challenged the management of technical and human resources in intensive care units (ICU) across the world. Several long-term predictors for COVID-19 disease progression have been discovered. However, predictors to support short-term planning of resources and medication that can be translated to future pandemics are still missing. A workflow was established to identify a predictor for short-term COVID-19 disease progression in the acute phase of intensive care patients to support clinical decision-making. MethodsThirty-two patients with SARS-CoV-2 infection were recruited on admission to the ICU and clinical data collected. During their hospitalization, plasma samples were acquired from each patient on multiple occasions, excepting one patient for which only one time point was possible, and the proteome (Inflammation, Immune Response and Organ Damage panels from Olink® Target 96), metabolome and lipidome (flow injection analysis and liquid chromatography-mass spectrometry) analyzed for each sample. Patient visits were grouped according to changes in disease severity based on their respiratory and organ function, and evaluated using a combination of statistical analysis and machine learning. The resulting short-term predictor from this multi-omics approach was compared to the human assessment of disease progression. Furthermore, the potential markers were compared to the baseline levels of 50 healthy subjects with no known SARS-CoV-2 or other viral infections. ResultsA total of 124 clinical parameters, 271 proteins and 782 unique metabolites and lipids were assessed. The dimensionality of the dataset was reduced, selecting 47 from the 1177 parameters available following down-selection, to build the machine learning model. Subsequently, two proteins (C–C motif chemokine 7 (CCL7) and carbonic anhydrase 14 (CA14)) and one lipid (hexosylceramide 18:2; O2/20:0) were linked to disease progression in the studied SARS-CoV-2 infections. Thus, a predictor delivering the prognosis of an upcoming worsening of the patient's condition up to five days in advance with a reasonable accuracy (79 % three days prior to event, 84 % four to five days prior to event) was found. Interestingly, the predictor's performance was complementary to the clinicians' capabilities to foresee a worsening of a patient. ConclusionThis study presents a workflow to identify omics-based biomarkers to support clinical decision-making and resource management in the ICU. This was successfully applied to develop a short-term predictor for aggravation of COVID-19 symptoms. The applied methods can be adapted for future small cohort studies.

Atrial fibrillation is not an independent determinant of 28-day mortality among critically III sepsis patients

This study was conducted to investigate the relationship between atrial fibrillation and the clinical prognosis of patients with sepsis in intensive care unit. A total of 21,538 sepsis patients were enrolled in the study based on the Medical Information Mart for Intensive Care IV database, of whom 6,759 had AF. Propensity score matching was used to compare the clinical characteristics and outcomes of patients with and without AF. Besides, the inverse probability of treatment weighting, univariate and multivariate Cox regression analyzes were performed. Of the 21,538 patients, 31.4% had AF. The prevalence of AF increased in a step-by-step manner with growing age. Patients with AF were older than those without AF. After PSM, 11,180 patients remained, comprising 5,790 matched pairs in both groups. In IPTW, AF was not associated with 28-day mortality [hazard ratio (HR), 1.07; 95% confidence interval (CI), 0.99–1.15]. In Kaplan-Meier analysis, it was not observed difference of 28-day mortality between patients with and without AF. AF could be associated with increased ICU LOS, hospital LOS and need for mechanical ventilation; however, it does not remain an independent short-term predictor of 28-day mortality among patients with sepsis after PSM with IPTW and multivariate analysis.

Two phases of online food delivery app users’ behavior in Greater Jakarta during the second year of the COVID-19 pandemic: Perceptions of food safety and hygiene

Background: The ongoing COVID-19 pandemic affects several aspects of food safety and food delivery hygiene for online food delivery applications (OFDAs). Several previous studies conducted at various times during the COVID-19 pandemic have yielded interesting results. This study aimed to determine and analyze whether perceptions of food safety and food delivery hygiene are short-term and long-term predictors of behavioral intention to use OFDAs. Methods: A quantitative approach and structural modeling were used in this study. SmartPLS is used to analyze five direct and two mediating effects of the variables used in this study. The data sample consists of OFDA users in Greater Jakarta during the second year of the COVID-19 pandemic. Results: Perceived food safety has a significant effect on behavioral intention to use these applications but does not affect behavioral intention to continue using these applications. Perceived food delivery hygiene does not influence using these apps for either short-term or long-term behavioral purposes. Experience with these apps related to food safety and food delivery hygiene during a pandemic can be indicative of their long-term viability. The mediating effect of behavioral intention to use on behavioral intention to continue using these apps affects perceived food safety but not food delivery hygiene. Conclusion: By emphasizing vaccination and boosting against COVID-19, providing hand sanitizer, and preserving the cleanliness and integrity of food packaging, the findings of this study can be put into practice towards the sustainable usage of OFDAs.

Predictors of In-Hospital Death in Patients With Acute Myocardial Infarction.

Factors such as age, vital signs, renal function, Killip class, cardiac arrest, elevated cardiac biomarker levels, and ST deviation predict survival in patients with acute myocardial infarction (AMI). However, the existing risk assessment tools lack comprehensive consideration of catheter-related factors, and short-term prognostic predictors are unknown. This study aimed to clarify in-hospital prognostic predictors in hospitalized patients with AMI. Five hundred and thirty-six patients who underwent percutaneous coronary intervention (PCI) for AMI were divided into non-survivor (n = 36) and survivor (n = 500) groups. Coronary risk factors, laboratory findings, angiographic findings, and clinical courses were compared between the two groups. Multiple logistic regression was used to analyze in-hospital death in pre- and post-PCI phases. In the pre-PCI phase, multiple logistic regression analysis revealed several predictors of in-hospital death, including systolic blood pressure [odds ratio (OR) = 0.985, p = 0.023)], Killip class ≥2 (OR = 14.051, p <0.001), and chronic kidney disease (OR = 4.859, p = 0.040). In the post-PCI phase, multiple logistic regression analysis revealed additional predictors of in-hospital death, including Killip class ≥2 (OR = 5.982, p = 0.039), presence of lesions in the left main trunk (OR = 51.381, p = 0.044), utilization of intra-aortic balloon pumps and percutaneous cardiopulmonary support (OR = 6.141, p = 0.016), and presence of multi-vessel disease (OR = 6.323, p = 0.022). Predictors of in-hospital death in AMI extend beyond conventional risk factors to include culprit lesions, mechanical support, and multi-vessel disease that manifest post-PCI.

EULAR points to consider for the definition of clinical and imaging features suspicious for progression from psoriasis to psoriatic arthritis

BackgroundThe transition from psoriasis (PsO) to psoriatic arthritis (PsA) and the early diagnosis of PsA is of considerable scientific and clinical interest for the prevention and interception of PsA.ObjectiveTo formulate...

Daily prediction of nonsuicidal self-injury among inpatients: The roles of suicidal thoughts, interpersonal difficulties, hopelessness, and affect.

Nonsuicidal self-injury (NSSI) is a major public health issue. Despite increased research efforts in clinical samples, we still have little understanding of the short-term correlates, predictors, and targets of NSSI among treatment-seeking individuals. The present study was designed to (a) evaluate how suicidal thoughts, interpersonal difficulties, hopelessness, and affective states are associated with same-day and next-day NSSI; (b) identify which factors may be effective targets in treatment through network modeling. Data from 1,265 psychiatric inpatients who self-injured throughout their visit to a psychiatric hospital self-reported their suicidal ideation, negative affect, and positive affect on a daily basis (in total 36,345 prospective reports). An additional 632 patients were also surveyed regarding feelings of hopelessness, wish to live, and interpersonal difficulties. Using multilevel structural equation modeling, we examined contemporaneous and time-lagged associations with NSSI. Multilevel network analyses assessed interconnectedness of daily predictors and were compared with a matched sample of 1,265 patients who did not self-injure during their stay. Increases in suicidal ideation were associated with increased probability of same-day and next-day self-injury, and an inverse relationship was observed for wish to live. Increases in positive affect were also significantly associated with decreased probability of next-day self-injury. Perceived burdensomeness had high centrality in network models, particularly among patients who self-injured, indicating it is susceptible to activation and directly associated with all predictors. Routine monitoring may improve prediction of when a patient is at short-term risk to self-injure and provides person-specific data that can assist in targeting risk and protective factors during treatment. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Dynamic Traffic Prediction-Based Energy Management of Connected Plug-In Hybrid Electric Vehicles with Long Short-Term State of Charge Planning

Vehicle electrification, automation, and connectivity in today's transportation require significant efforts in control design to meet conflicting goals of energy efficiency, traffic safety, as well as comfort. The rapid development of intelligent transportation systems (ITS) and the rapid growth of connectivity technologies enable vehicles to receive more information about traffic conditions, which provides a reliable solution for the energy management of plug-in hybrid electric vehicles (PHEVs). This article proposes a predictive energy management strategy (EMS) for connected PHEV based on real-time dynamic traffic prediction. First, the future traffic information is predicted by establishing a wavelet neural network (WNN). Thus, the global driving condition can be predicted. Then, the particle swarm optimization (PSO) algorithm is used to optimize the parameters of WNN to plan a global battery state-of-charge (SOC) reference. Second, a long short-term memory-based velocity predictor is proposed for the predictive EMS, by planning SOC over a prediction horizon based on the global SOC reference. Finally, the performance of the proposed EMS with WNN and PSO-WNN is verified by the actual traffic data. The results show that it can improve the fuel economy by 17.57% and 28.19%, respectively.