Predictive Utility Research Articles

How Biologics Have Changed the Drug Discovery Landscape.

Advances in molecular biology and molecular genetics as well as major scientific breakthroughs in immunology and oncology have led to the rapid growth of biologic therapeutics. Their success has resulted in significant changes to virtually every step in the drug discovery and development process. Biologics are produced by living organisms, and screening libraries are generated by immunization or phage display. Lead optimization utilizes sophisticated protein engineering to improve drug-like properties and targeting specificity. The manufacturing process for biologics is complex and requires highly specialized facilities. Determination of pharmacology and safety must overcome the complications associated with species specificity. Initial clinical testing must proceed more slowly and carefully due to the limited predictive utility of preclinical data. In summary, the drug discovery and development process has been dramatically altered by biologic therapeutics and will continue to evolve with the introduction of messenger RNA-based therapeutics and the application of artificial intelligence.

Comparison of brief olfactory and cognitive assessments to neuroimaging biomarkers in the prediction of cognitive decline and dementia in the MCSA cohort.

We evaluated impaired odor identification and global cognition as simple, cost-effective alternatives to neuroimaging biomarkers to predict cognitive decline and dementia in the Mayo Clinic Study of Aging. Six hundred forty-seven participants (mean 8.1, standard deviation 3.4 years' follow-up) had the following baseline procedures: modified Blessed Information Memory Concentration Test (BIMCT), 12-item Brief Smell Identification Test (BSIT), structural brain magnetic resonance imaging (MRI), and positron emission tomography (PET) imaging with 11C-Pittsburgh compound B (11C-PiB) and fluorodeoxyglucose (FDG; subset). Cognitive decline developed in 102 participants and dementia in 34 participants. In survival analyses, PiB PET showed robust prediction for cognitive decline. Impaired BSIT, impaired BIMCT, MRI, and FDG measures were also significant predictors. The combination of demographics + BSIT + BIMCT showed strong predictive utility (C-index 0.81), similar to demographics + PiB PET (C-index 0.80). Similar but stronger results were obtained for prediction of dementia. Impairment in both odor identification test and global cognition was comparable to PiB PET for predicting cognitive decline and dementia. In 647 participants in the population-based Mayo Clinic Study of Aging, several clinical markers and biomarkers each predicted cognitive decline or dementia during an average 8 years of follow-up. The combination of the demographic variables of age, sex, and education with a brief odor identification test (BSIT) and a global cognitive test (Blessed Information Memory Concentration Test) showed strong predictive utility (C-index 0.81) for cognitive decline that was similar to the demographic variables combined with Pittsburgh Compound B amyloid imaging (C-index 0.80). Combining a brief odor identification test with a brief cognitive test needs consideration as a simple, cost-effective option in the clinical assessment of individuals at risk of cognitive decline and dementia, as well as a potential tool to identify individuals who may benefit from disease-modifying treatments and to screen participants for prevention trials.

Update and validation of the Beliefs about Losing Control Inventory-II (BALCI-II): a psychometric investigation

ABSTRACT The Beliefs about Losing Control Inventory (BALCI) was developed to assess negative beliefs about losing control in obsessive-compulsive disorder (OCD). Since its creation, research and theoretical work support negative beliefs about losing control as a potential transdiagnostic construct. The present study revised and expanded the original BALCI to be more inclusive of control-related concerns beyond those that would be expected in OCD (e.g. concerns about losing control over how one comes across to others in social anxiety disorder; SAD). Undergraduate students (N = 440) completed a questionnaire battery including the BALCI-II item pool. An exploratory factor analysis of the 32-item BALCI-II supported a four-factor solution. Three of the identified factors capture the feared consequences of losing control: 1) overwhelming emotions, 2) dangerous behaviour, and 3) madness. The fourth factor captures inflated beliefs about probability/severity of those losses. The BALCI-II was found to have good convergent and divergent validity, good to excellent internal, and retest reliability and was shown to have predictive utility in both OCD and SAD, above and beyond existing disorder-specific maladaptive belief domains. Results suggest the BALCI-II is an improvement over the previous version and supports the relevance of these beliefs beyond OCD.

Measles and Pertussis outbreaks in England and Wales: a time-series analysis

Background Vaccine coverage for common infectious diseases such as Measles and Pertussis (also known as whooping cough) have been declining in England and Wales since 2014. Consequently, significant increases in Measles and Pertussis cases are observed in the community. Aim To explore whether Google Trends offers a predictive utility as a health surveillance tool for Meases and Pertussis in England and Wales. Design and Setting Google search data related to Measles and Pertussis, including common associated symptoms, were downloaded for 52 weeks from 07/01/2023 – 07/01/2024. Measles and Pertussis case data were retrieved from the weekly Notification of Infectious Disease (NOID) reports. Methods The associations between searching and case data were explored using a time-series analyses, including cross-correlations, Prais-Winsten regression and joinpoint analysis. Results Significant cross-correlations were found for Measles cases and “measles” searching (r=.41) at a lag of -1 week. For Pertussis cases, searching for “whooping cough” (r=.31), “cough” (r=.39), “100 day cough” (r.41) and “vomiting” (r=.42) were significantly correlated at a lag of -3 to -2 weeks. In multivariable regression, “measles” remained significantly associated with Measles cases (β=.24, SE=.33, p=.02) as did “whooping cough” (β=.71, SE=.27, p=.01) and “cough” (β=1.99, SE=.54, p=.001) for pertussis. Conclusion Increases in Measles and Pertussis cases follow increases in online searches for both diseases and selected respective symptoms. Further work is required to explore how GT can be used in conjunction with other health surveillance systems to monitor or even predict disease outbreaks, to better target public health interventions.

Atypical Multifocal Granular Cell Tumor with FLT3 Y842C Somatic Mutation: A case report and a review of the literature.

Granular cell tumors (GCT) are predominantly benign neoplasms composed by cells with abundant eosinophilic granular cytoplasm. Although the majority of GCTs exhibit a benign clinical course, a minority display cytological atypia and may exhibit aggressive, cancer-like behavior. Definitive evidence of malignancy in GCTs is reliably established only through the presence of metastasis. Addi- tionally, a subset of GCTs demonstrates a high rate of recurrence, underscoring the need for better prog- nostic markers. Therefore, it is crucial to identify molecular markers associated with aggressive behavior in GCTs. Molecular analysis may be particularly beneficial in cases exhibiting cytological atypia to in- form clinical outcome prognostication and guide therapeutic strategies. In this case report, a 45-year-old female with multiple gastrointestinal GCTs is pre- sented. The patient did not have any genetic syndromes commonly associated with GCT, such as neu- rofibromatosis type 1, Noonan syndrome or LEOPARD syndrome. The tumors not only demonstrated nuclear atypia, but also harbored a unique FLT3 Y842C somatic alteration identified by next-genera- tion sequencing. The patient remains asymptomatic and under endoscopic surveillance two years after diagnosis and complete resection of the neoplasms. We presented an exceedingly rare case of multifocal atypical GCT in an adult without any previously known genetic syndrome. A tumoral FLT3 Y842C point mutation not previously reported in GCT was discovered. Although the precise significance of this finding is uncertain, FLT3 Y842C has been cataloged as likely pathogenic in ClinVar. This report underscores the potential predictive utility of next-generation sequencing in the characterization and management of rare neoplasms.

Multi-ancestry GWAS meta-analyses of lung cancer reveal susceptibility loci and elucidate smoking-independent genetic risk

Lung cancer remains the leading cause of cancer mortality, despite declining smoking rates. Previous lung cancer GWAS have identified numerous loci, but separating the genetic risks of lung cancer and smoking behavioral susceptibility remains challenging. Here, we perform multi-ancestry GWAS meta-analyses of lung cancer using the Million Veteran Program cohort (approximately 95% male cases) and a previous study of European-ancestry individuals, jointly comprising 42,102 cases and 181,270 controls, followed by replication in an independent cohort of 19,404 cases and 17,378 controls. We then carry out conditional meta-analyses on cigarettes per day and identify two novel, replicated loci, including the 19p13.11 pleiotropic cancer locus in squamous cell lung carcinoma. Overall, we report twelve novel risk loci for overall lung cancer, lung adenocarcinoma, and squamous cell lung carcinoma, nine of which are externally replicated. Finally, we perform PheWAS on polygenic risk scores for lung cancer, with and without conditioning on smoking. The unconditioned lung cancer polygenic risk score is associated with smoking status in controls, illustrating a reduced predictive utility in non-smokers. Additionally, our polygenic risk score demonstrates smoking-independent pleiotropy of lung cancer risk across neoplasms and metabolic traits.

When are predictions useful? A new method for evaluating epidemic forecasts

BackgroundCOVID-19 will not be the last pandemic of the twenty-first century. To better prepare for the next one, it is essential that we make honest appraisals of the utility of different responses to COVID. In this paper, we focus specifically on epidemiologic forecasting. Characterizing forecast efficacy over the history of the pandemic is challenging, especially given its significant spatial, temporal, and contextual variability. In this light, we introduce the Weighted Contextual Interval Score (WCIS), a new method for retrospective interval forecast evaluation.MethodsThe central tenet of the WCIS is a direct incorporation of contextual utility into the evaluation. This necessitates a specific characterization of forecast efficacy depending on the use case for predictions, accomplished via defining a utility threshold parameter. This idea is generalized to probabilistic interval-form forecasts, which are the preferred prediction format for epidemiological modeling, as an extension of the existing Weighted Interval Score (WIS).ResultsWe apply the WCIS to two forecasting scenarios: facility-level hospitalizations for a single state, and state-level hospitalizations for the whole of the United States. We observe that an appropriately parameterized application of the WCIS captures both the relative quality and the overall frequency of useful forecasts. Since the WCIS represents the utility of predictions using contextual normalization, it is easily comparable across highly variable pandemic scenarios while remaining intuitively representative of the in-situ quality of individual forecasts.ConclusionsThe WCIS provides a pragmatic utility-based characterization of probabilistic predictions. This method is expressly intended to enable practitioners and policymakers who may not have expertise in forecasting but are nevertheless essential partners in epidemic response to use and provide insightful analysis of predictions. We note that the WCIS is intended specifically for retrospective forecast evaluation and should not be used as a minimized penalty in a competitive context as it lacks statistical propriety. Code and data used for our analysis are available at https://github.com/maximilian-marshall/wcis.

The SpO2/FiO2 Ratio Combined with Prognostic Scores for Pneumonia and COVID-19 Increases Their Accuracy in Predicting Mortality of COVID-19 Patients

Background: Identifying high-risk COVID-19 patients is critical for emergency department decision-making. Our study’s primary objective was to identify new independent predictors of mortality and their predictive utility in combination with traditional pneumonia risk assessment scores and new risk scores for COVID-19 developed during the pandemic. Methods: A retrospective study was performed in two Italian University Hospitals. A multivariable logistic model was used to locate independent parameters associated with mortality. Results: Age, PaO2/FiO2, and SpO2/FiO2 ratios were found to be independent parameters associated with mortality. This study found that the Pneumonia Severity Index (PSI) was superior to many of the risk scores developed during the pandemic, for example, the International Severe Acute Respiratory Infection Consortium Coronavirus Clinical Characterisation Consortium (ISARIC 4C) (AUC 0.845 vs. 0.687, p < 0.001), and to many of the risk scores already in use, for example, the National Early Warning Score 2 (NEWS2) (AUC 0.845 vs. 0.589, p < 0.001). Furthermore, our study found that the Pneumonia Severity Index had a similar performance to other risk scores, such as CRB-65 (AUC 0.845 vs. 0.823, p = 0.294). Combining the PaO2/FiO2 or SpO2/FiO2 ratios with the risk scores analyzed improved the prognostic accuracy. Conclusions: Adding the SpO2/FiO2 ratio to the traditional, validated, and already internationally known pre-pandemic prognostic scores seems to be a valid and rapid alternative to the need for developing new prognostic scores. Future research should focus on integrating these markers into existing pneumonia scores to improve their prognostic accuracy.

Prediction of Low-Visibility Events by Integrating the Potential of Persistence and Machine Learning for Aviation Services

Fog typically results in reduced atmospheric visibility. Severely limited visibility has a significant impact on transportation, particularly the operations of aircraft. Precise forecasts of low visibility are essential for aviation services, primarily for the efficient planning of airport activities. Despite the utilization of sophisticated numerical weather prediction (NWP) models, the prediction of fog and limited visibility remains challenging. The intricacy of fog prediction is due to limitations in understanding the micro-scale factors that lead to fog genesis, intensification, persistence, and dissipation. This study investigates the occurrence of fog (surface visibility <1000 m) and dense fog (surface visibility < 200 m) throughout the climatological low-visibility months (November to February) to analyze the persistence of low-visibility events and predict them in the specific conditions of the frog prone Indo-Gangetic Plain (IGP) regions. A representative station, Jay Prakash Narayan International (JPNI) Airport in Patna, India, has been considered given the availability of instrumental quality datasets. The analysis investigates the long-term and short-term persistence and prediction of the series using a diverse variety of machine learning (ML) algorithms. To conduct a comprehensive analysis over an extended period, detrended fluctuation analysis (DFA) is employed to determine the similarities between the time series of large-scale fog and dense fog. A Markov chain model is used to look at the binary time series and figure out how long low-visibility events (like fog and dense fog) last in the short term ( 1-5 hours). Ultimately, we analyze a short-term forecast (Nowcast) with a lead time of one to five hours for instances of low visibility (fog or dense fog). This nowcasting is generated utilizing diverse methodologies, including Markov chain models, persistence analysis, and machine learning (ML) methods. Finally, establish that the most favorable and reliable results in this prediction problem are attained by employing a Mixture of Experts model that integrates persistence-based methods and ML algorithms.

Early clinical predictors of infected pancreatic necrosis: a multicentre cohort study

BackgroundInfected pancreatic necrosis (IPN) exacerbates complications in patients with acute pancreatitis (AP), increasing mortality rates if not treated promptly. We aimed to evaluate the predictive value of clinical characteristics within...

Transcription Factor Forkhead Box Protein 3 (FOXP3) as a Prognostic Indicator for Postoperative Outcomes in Patients with Breast Cancer: Establishment of a Prognostic Nomogram.

The current investigation is to assess FOXP3 expression in breast cancer patients and evaluate the predictive significance of FOXP3. A cohort of 313 cases between January 2015 and November 2015 were enrolled this research. Immunohistochemistry (IHC) assay was utilized to detect the expression levels of FOXP3 in primary breast carcinoma specimens. These patients were separated into two groups by semiquantitative scoring approach. Chi-square test and Fisher's exact test were conducted to investigate the correlations between FOXP3 expression in tumors and clinicopathological variables. Kaplan-Meier method and Log rank test were utilized to generate survival curves for disease-free survival (DFS) and overall survival (OS). The independent factors were examined using Cox regression analysis. Nomogram models were created for assessing DFS and OS rates. Depending on the levels of FOXP3 expression in tumors, these patients were categorized into two groups: low FOXP3 expression (174 cases) and high FOXP3 expression (139 cases). The patients exhibiting low levels of FOXP3 expression in tumors demonstrated a longer survival duration contrasted with those with high expression (DFS: 88.75 vs 65.87 months, χ2=36.1100, P<0.0001; OS: 89.70 vs 78.37 months, χ2=32.4900, P<0.0001). Multivariate analysis revealed that FOXP3 was a significant prognostic factor [DFS: hazard ratio (HR): 2.822, 95% CI: 1.595-4.992, P<0.0001; OS: HR: 3.232, 95% CI: 1.812-5.763, P<0.0001]. The good predictive clinical utility of FOXP3-based nomograms within the threshold probability range for different survival rates was demonstrated by calibration curve and decision curve analyses. FOXP3 expression serves as a crucial prognostic indicator in breast cancer patients, and may aid preoperative evaluation in clinical practice.

Modifications to the National Early Warning Score: a scoping review protocol

IntroductionThe National Early Warning Score (NEWS/2) system was developed to enable the detection and early intervention of patients at risk of clinical deterioration. It has demonstrated good accuracy in identifying...

Predictive utility of a combination of the modified caprini risk assessment score and D-dimer for the evaluation and management of lower extremity venous thrombosis after lung cancer surgery

ObjectiveThe objective of this study was to examine the utility of a combination of the modified Caprini score and D-dimer levels for the evaluation and management of lower extremity venous thrombosis following lung cancer surgery. The purpose was to offer insights for developing clinical intervention programs.MethodsThe study sample consisted of 224 patients who underwent surgery for lung cancer at the First Central Hospital of Baoding City. General patient data and D-dimer levels on the first day post-surgery were collected. The modified Caprini risk assessment score was calculated. All patients underwent ultrasonography of the lower limb veins before and after surgery to identify venous thrombosis in the lower limb veins. Differences in lower extremity venous thrombosis and D-dimer levels among patients in various modified Caprini score groups were compared and analyzed.ResultsBased on the modified Caprini risk assessment score, all patients were categorized into three groups: the low-risk, medium-risk, and high-risk groups. The groups did not differ significantly in terms of age, but the differences in the rates of lower extremity venous thrombosis in the low, intermediate, and high-risk Caprini risk groups (16.5%, 19.2%, and 37.1%, respectively) were statistically significant. Out of the total 224 patients, 47 (21%) were diagnosed with venous thromboembolisms post-surgery, and all of them had thrombosis of the intermuscular veins of the lower extremity. The difference in the modified Caprini risk assessment score between patients with and without lower extremity venous thrombosis was statistically significant (P = 0.035), as were the postoperative D-dimer levels (1.28 ± 1.64 vs. 2.69 ± 2.77, respectively; P < 0.05) between these two groups of patients. The modified Caprini risk assessment score showed an association with lower extremity venous thrombosis (r = 0.15, P = 0.56) with an area under the receiver operating characteristic curve (AUC) of 0.59.ConclusionIn this study, we found that combining the modified Caprini risk assessment score with D-dimer measurements enhanced the accuracy of assessing the severity of deep vein thrombosis (DVT). This combination can be beneficial in evaluating thrombosis risk post-lung cancer surgery and holds significant clinical utility.

Glomerular Filtration Rate by Differing Measures and Prediction Utility of Peripheral Artery Disease

Glomerular Filtration Rate by Differing Measures and Prediction Utility of Peripheral Artery Disease

Longitudinal evaluation of individuals with severe alpha-1 antitrypsin deficiency (Pi*ZZ genotype)

Homozygous Pi*Z mutation in alpha-1 antitrypsin (Pi*ZZ genotype) predisposes to pulmonary loss-of-function and hepatic gain-of-function injury. To facilitate selection into clinical trials typically targeting only one organ, we systematically evaluated an international, multicenter, longitudinal, Pi*ZZ cohort to uncover natural disease course and surrogates for future liver- and lung-related endpoints. Cohort 1 recruited 737 Pi*ZZ individuals from 25 different centers without known liver comorbidities that received a baseline clinical and laboratory assessment as well as liver stiffness measurement (LSM). A follow-up interview was performed after at least six months. Cohort 2 consisted of 135 Pi*ZZ subjects without significant liver fibrosis, who received a standardized baseline and follow-up examination at least two years later, both including LSM. During 2634 patient-years of follow-up, 39 individuals died, with liver and lung being responsible for 46% and 36% of deaths, respectively. 41 Pi*ZZ subjects who developed a hepatic endpoint presented with significantly higher baseline liver fibrosis surrogates, i.e., LSM (24 vs. 5 kPa, p<.001) and AST-to-platelet ratio index (APRI, 1.1 vs. 0.3 units, p<.001). Liver-related endpoints within five years were most accurately predicted by LSM (area under the curve [AUC] 0.95) followed by APRI (0.92). Baseline lung parameters displayed only a moderate predictive utility for lung-related endpoints within five years (FEV1 AUC 0.76). Fibrosis progression in those with no/mild fibrosis at baseline was rare and primarily seen in those with preexisting risk factors. Non-invasive liver fibrosis surrogates accurately stratify liver-related risks in Pi*ZZ individuals. Our findings have direct implications for routine care and future clinical trials of Pi*ZZ patients.

Technology acquisitions and investor expectations: Reputation and expectancy violation perspectives

AbstractEmploying the organizational reputation lens and expectancy violation theory (EVT), I examine how financial market investors (investors) affect the technology acquisition activity and the likelihood of survival of firms facing technological change. I theorize that when a firm has a growth reputation, that is, investors expect revenue growth in future periods, the likelihood of making an acquisition will increase in anticipation of a positive expectancy violation on the part of investors. In contrast, for a firm that has an income reputation, that is, investors expect shareholder returns in future periods, the likelihood of making an acquisition will decrease in avoidance of a negative expectancy violation on the part of investors. I predict a novel moderating effect of investor expectations—a firm's acquisition activity will exert a stronger positive effect on its likelihood of surviving technological change when investor expectations are growth‐oriented, that is, when there is a positive expectancy violation. Using a multi‐industry sample of industry convergence, a salient form of technological change, I find support for my theoretical predictions. I advance research that examines the reactions of investors to firm strategies during technological change to the domain of technology acquisitions, a salient strategic decision. I also expand the predictive utility of the reputation lens and EVT to the context of firm survival during technological change.

Body temperature, systemic inflammation and risk of adverse events in patients with acute coronary syndromes.

Inflammatory processes can trigger acute coronary syndromes (ACS) which may increase core body temperature (BT), a widely available low-cost marker of systemic inflammation. Herein, we aimed to delineate baseline characteristics of ST-segment elevation myocardial infarction (STEMI) and non-ST-segment elevation ACS (NSTE-ACS) patients stratified by initial BT and to assess its predictive utility towards major adverse cardiovascular events (MACE) after the index ACS. From 2012 until 2017, a total of 1044 ACS patients, 517 with STEMI and 527 with NSTE-ACS, were prospectively recruited at the University Hospital Zurich. BT was measured by digital tympanic thermometer along with high-sensitivity C-reactive protein (hs-CRP) and cardiac troponin-T (hs-cTnT) levels prior to coronary angiography. Patients were stratified according to initial BT and uni- and multivariable regression models were fit to assess associations of BT with future MACE risk. Among patients with STEMI, BT was not predictive of 1-year MACE, but a U-shaped relationship between BT and MACE risk was noted in those with NSTE-ACS (p = .029), translating into a 2.4-fold (HR, 2.44, 95% CI, 1.16-5.16) increased 1-year MACE risk in those with BT >36.8°C (reference: 36.6-36.8°C). Results remained robust in multivariable-adjusted analyses accounting for sex, age, diabetes, renal function and hs-cTnT. However, when introducing hs-CRP, the BT-MACE association did not prevail. In prospectively recruited patients with ACS, initial BT shows a U-shaped relationship with 1-year MACE risk among those with NSTE-ACS, but not in those with STEMI. BT is a broadly available low-cost marker to identify ACS patients with high inflammatory burden, at high risk for recurrent ischaemic events, and thus potentially suitable for an anti-inflammatory intervention. ClinicalTrials.gov Identifier: NCT01000701.

Detecting Narcissistic Grandiosity in a Job Interview: The Validation of the Narcissism Interview Scale for Employment

Although employee selection is typically oriented toward the assessment of knowledge, skills, and abilities to identify employees who will complement such an environment, it is perhaps equally important to distinguish employees with the potential to disrupt it. Workers high in narcissistic grandiosity tend to abuse their power and control for personal gain, engage in abusive behaviors toward others, and disobey organizational policies. Across four studies, we sought to develop the Narcissism Interview Scale for Employment (NISE) to assess narcissistic grandiosity. Study 1 created interview questions that elicited responses with narcissistic grandiosity content, structured as both behavioral/situational and work-specific. Study 2 identified the best performing items and developed rating materials. Study 3 demonstrated the NISE is associated with traditional survey assessments of narcissistic grandiosity and predicted interpersonal aggression. Study 4 demonstrated that applicants are likely to perceive the NISE at least as favorably as other popular interview questions. Overall, the results showed that the NISE may be incorporated into the interview process to assess applicant narcissistic grandiosity tendencies, but additional research is needed to further establish the construct validity of the instrument, clarify applicant reactions to its use, and assess its predictive utility across a variety of work contexts.

Predictive value of serum interleukin-6 for neonatal encephalopathy outcomes.

Serum interleukin-6 (IL-6) may predict adverse outcomes of neonatal encephalopathy (NE); however, limited data regarding the predictive utility of IL-6 during neurodevelopmental follow-up are available. We aimed to determine the utility of IL-6 for predicting adverse outcomes at 18 to 22 months of age. Eighty-seven patients with NE who received therapeutic hypothermia were enrolled in this study. Serial serum IL-6 levels during the first 3 postnatal days were collected. Patients were classified into three groups: 1) death, 2) survival with moderate to severe neurodevelopmental disability (NDD) at 18-22 months of age, and 3) survival without NDD (favorable outcome). The predictive ability of IL-6 was determined by the area under the receiver-operating characteristic curve (AUC). Serial IL-6 data of 80 patients with NE were available and showed peak levels on postnatal day 1; these levels gradually decreased toward day 3. By 18-22 months of age, 13 and 17 patients died and experienced moderate to severe NDD without death, respectively. Fifty patients experienced favorable outcomes. Higher IL-6 levels on day 1 predicted the composite adverse outcome (including death and survival with NDD; n = 30; AUC, 0.648). Higher IL-6 levels on day 1 predicted death (n = 13; AUC, 0.799), whereas higher IL-6 levels on day 1 predicted survival with NDD (n = 17; AUC, 0.536). The AUC of IL-6 that predicted survival with NDD was lower than the AUC of IL-6 that predicted death; therefore, IL-6 may have insufficient utility for predicting NDD without death.

Development, assessment and validation of a novel nomogram model for predicting stroke mimics in stroke center：A single-center observational study

BackgroundEarly recognition and prediction of stroke mimics (SM) can avoid inappropriate recanalization therapy and delay in the management of SM etiology. The purpose of this study is to screen the predictors for SM and develop a novel predictive nomogram model for predicting SM. Meanwhile, the diagnostic performance of the nomogram model was evaluated and validated. The diagnostic efficacy of the nomogram model was also compared with four other SM structured scales. MethodsThe clinical data of eligible patients were retrospectively enrolled as training datasets from January 2020 to December 2021; and the clinical data of eligible patients were prospectively enrolled as validation datasets from February to December 2022 in stroke center, Shengjing hospital, respectively. Univariate analysis and Lasso regression were used to select the optimal predictors for the training set, and a nomogram model was constructed by multivariate logistics regression, predictive scoring based on nomogram model is performed for each subject suffering from suspected acute ischemic stroke. Area under the curve (AUC), Hosmer-Lemeshow goodness-of-fit test, Calibration curve, decision curve analysis (DCA), clinical impact curve (CIC) analysis and bootstrap sampling were performed to assess and validate the predictive performance and clinical utility of the nomogram model, and the DeLong test was used to compare the overall diagnostic performance of the nomogram model with the other four structured SM scales. The Delong test was also conducted to assess the external reliability of the SM nomogram model by comparing the predictive diagnostic performance of the validation set with the training set. Additionally, the Calibration curve was utilized to evaluate the diagnostic calibration capability of the SM nomogram model in the validation set. Results703 eligible patients (68 with SM, accounting for 9.7 %) were assigned to the training set, while 301 patients (26 with SM, accounting for 8.6 %) were assigned to the validation set. A nomogram model was then developed using these six parameters (SBP, history of epilepsy, isolated dizziness, isolated sensory impairment, headache, and absence of speech impairment symptoms), a dynamic web-based version of the nomogram was subsequently created. Comparing with four other scales, the nomogram model showed the highest overall diagnostic performance (AUC = 0.929, 95%CI = 0.908–0.947). The Hosmer-Lemeshow goodness-of-fit test was conducted to assess the agreement between the predicted SM values from the model and the observed SM values. The results of the test indicated a favorable consistency (χ2 = 9.299, P = 0.3177) between the predicted and observed SM. The results obtained from the analysis of the Calibration curve, DCA curve, and CIC analysis suggested that the nomogram possesses a favorable predictive capacity and superior clinical usefulness. Furthermore, the external validation demonstrated that there is no significant difference in the overall predictive diagnostic performance between the validation set and training set (0.929 vs 0.910, P > 0.05), thereby confirming the favorable stability of the nomogram model. ConclusionOur study firstly proposed a nomogram prediction approach based on the clinical features of SM, which could effectively predict the occurrence of SM. The utilization of the nomogram in stroke center proves advantageous for the identification and evaluation of SM, thereby enhancing diagnostic decision-making and strategies employed for suspected acute stroke patients.