scholarly journals Predicting persistence of hallucinations from childhood to adolescence

2021 ◽  
pp. 1-8
Author(s):  
Lisa R. Steenkamp ◽  
Henning Tiemeier ◽  
Laura M. E. Blanken ◽  
Manon H. J. Hillegers ◽  
Steven A. Kushner ◽  
...  
Keyword(s):  
Prediction Model ◽  
Stressful Life Events ◽  
Prediction Models ◽  
Mental Health Problems ◽  
Large Population ◽  
Area Under The Curve ◽  
Parental Psychopathology ◽  
Self Esteem ◽  
Psychotic Experiences ◽  
Verbal Iq

Summary Background Psychotic experiences predict adverse health outcomes, particularly if they are persistent. However, it is unclear what distinguishes persistent from transient psychotic experiences. Aims In a large population-based cohort, we aimed to (a) describe the course of hallucinatory experiences from childhood to adolescence, (b) compare characteristics of youth with persistent and remittent hallucinatory experiences, and (c) examine prediction models for persistence. Method Youth were assessed longitudinally for hallucinatory experiences at mean ages of 10 and 14 years (n = 3473). Multi-informant-rated mental health problems, stressful life events, self-esteem, non-verbal IQ and parental psychopathology were examined in relation to absent, persistent, remittent and incident hallucinatory experiences. We evaluated two prediction models for persistence with logistic regression and assessed discrimination using the area under the curve (AUC). Results The persistence rate of hallucinatory experiences was 20.5%. Adolescents with persistent hallucinatory experiences had higher baseline levels of hallucinatory experiences, emotional and behavioural problems, as well as lower self-esteem and non-verbal IQ scores than youth with remittent hallucinatory experiences. Although the prediction model for persistence versus absence of hallucinatory experiences demonstrated excellent discriminatory power (AUC-corrected = 0.80), the prediction model for persistence versus remittance demonstrated poor accuracy (AUC-corrected = 0.61). Conclusions This study provides support for the dynamic expression of childhood hallucinatory experiences and suggests increased neurodevelopmental vulnerability in youth with persistent hallucinatory experiences. Despite the inclusion of a wide array of psychosocial parameters, a prediction model discriminated poorly between youth with persistent versus remittent hallucinatory experiences, confirming that persistent hallucinatory experiences are a complex multifactorial trait.

scholarly journals Development and validation of circulating CA125 prediction models in postmenopausal women

2019 ◽  
Vol 12 (1) ◽  
Author(s):  
Naoko Sasamoto ◽  
Ana Babic ◽  
Bernard A. Rosner ◽  
Renée T. Fortner ◽  
Allison F. Vitonis ◽  
...  
Keyword(s):  
Ovarian Cancer ◽  
Cancer Screening ◽  
Prediction Model ◽  
Postmenopausal Women ◽  
Prediction Models ◽  
Smoking Status ◽  
Large Population ◽  
External Validation ◽  
Validation Dataset ◽  
Ovarian Cancer Screening

Abstract Background Cancer Antigen 125 (CA125) is currently the best available ovarian cancer screening biomarker. However, CA125 has been limited by low sensitivity and specificity in part due to normal variation between individuals. Personal characteristics that influence CA125 could be used to improve its performance as screening biomarker. Methods We developed and validated linear and dichotomous (≥35 U/mL) circulating CA125 prediction models in postmenopausal women without ovarian cancer who participated in one of five large population-based studies: Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO, n = 26,981), European Prospective Investigation into Cancer and Nutrition (EPIC, n = 861), the Nurses’ Health Studies (NHS/NHSII, n = 81), and the New England Case Control Study (NEC, n = 923). The prediction models were developed using stepwise regression in PLCO and validated in EPIC, NHS/NHSII and NEC. Result The linear CA125 prediction model, which included age, race, body mass index (BMI), smoking status and duration, parity, hysterectomy, age at menopause, and duration of hormone therapy (HT), explained 5% of the total variance of CA125. The correlation between measured and predicted CA125 was comparable in PLCO testing dataset (r = 0.18) and external validation datasets (r = 0.14). The dichotomous CA125 prediction model included age, race, BMI, smoking status and duration, hysterectomy, time since menopause, and duration of HT with AUC of 0.64 in PLCO and 0.80 in validation dataset. Conclusions The linear prediction model explained a small portion of the total variability of CA125, suggesting the need to identify novel predictors of CA125. The dichotomous prediction model showed moderate discriminatory performance which validated well in independent dataset. Our dichotomous model could be valuable in identifying healthy women who may have elevated CA125 levels, which may contribute to reducing false positive tests using CA125 as screening biomarker.

Predicting poor outcome in patients with suspected COVID-19 presenting to the Emergency Department (COVERED) – Development, internal and external validation of a prediction model

2021 ◽  
Vol 20 (1) ◽  
pp. 4-14
Author(s):  
K. Azijli ◽  
◽  
A.W.E. Lieveld ◽  
S.F.B. van der Horst ◽  
N. de Graaf ◽  
...  
Keyword(s):  
Emergency Department ◽  
Prediction Model ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
External Validation ◽  
Model Performance ◽  
Area Under The Curve ◽  
Brier Score ◽  
Multi Centre Study ◽  
Poor Outcome

Background: A recent systematic review recommends against the use of any of the current COVID-19 prediction models in clinical practice. To enable clinicians to appropriately profile and treat suspected COVID-19 patients at the emergency department (ED), externally validated models that predict poor outcome are desperately needed. Objective: Our aims were to identify predictors of poor outcome, defined as mortality or ICU admission within 30 days, in patients presenting to the ED with a clinical suspicion of COVID-19, and to develop and externally validate a prediction model for poor outcome. Methods: In this prospective, multi-centre study, we enrolled suspected COVID-19 patients presenting at the EDs of two hospitals in the Netherlands. We used backward logistic regression to develop a prediction model. We used the area under the curve (AUC), Brier score and pseudo-R2 to assess model performance. The model was externally validated in an Italian cohort. Results: We included 1193 patients between March 12 and May 27 2020, of whom 196 (16.4%) had a poor outcome. We identified 10 predictors of poor outcome: current malignancy (OR 2.774; 95%CI 1.682-4.576), systolic blood pressure (OR 0.981; 95%CI 0.964-0.998), heart rate (OR 1.001; 95%CI 0.97-1.028), respiratory rate (OR 1.078; 95%CI 1.046-1.111), oxygen saturation (OR 0.899; 95%CI 0.850-0.952), body temperature (OR 0.505; 95%CI 0.359-0.710), serum urea (OR 1.404; 95%CI 1.198-1.645), C-reactive protein (OR 1.013; 95%CI 1.001-1.024), lactate dehydrogenase (OR 1.007; 95%CI 1.002-1.013) and SARS-CoV-2 PCR result (OR 2.456; 95%CI 1.526-3.953). The AUC was 0.86 (95%CI 0.83-0.89), with a Brier score of 0.32 and, and R2 of 0.41. The AUC in the external validation in 500 patients was 0.70 (95%CI 0.65-0.75). Conclusion: The COVERED risk score showed excellent discriminatory ability, also in external validation. It may aid clinical decision making, and improve triage at the ED in health care environments with high patient throughputs.

scholarly journals Individual participant data validation of the PICNICC prediction model for febrile neutropenia

2019 ◽  
Vol 105 (5) ◽  
pp. 439-445 ◽  
Author(s):  
Bob Phillips ◽  
Jessica Elizabeth Morgan ◽  
Gabrielle M Haeusler ◽  
Richard D Riley
Keyword(s):  
Prediction Model ◽  
Prediction Models ◽  
Meta Analysis ◽  
Characteristic Curve ◽  
Area Under The Curve ◽  
Infectious Complications ◽  
Cancer Therapies ◽  
Individual Participant Data ◽  
Documented Infection ◽  
Individual Participant

BackgroundRisk-stratified approaches to managing cancer therapies and their consequent complications rely on accurate predictions to work effectively. The risk-stratified management of fever with neutropenia is one such very common area of management in paediatric practice. Such rules are frequently produced and promoted without adequate confirmation of their accuracy.MethodsAn individual participant data meta-analytic validation of the ‘Predicting Infectious ComplicatioNs In Children with Cancer’ (PICNICC) prediction model for microbiologically documented infection in paediatric fever with neutropenia was undertaken. Pooled estimates were produced using random-effects meta-analysis of the area under the curve-receiver operating characteristic curve (AUC-ROC), calibration slope and ratios of expected versus observed cases (E/O).ResultsThe PICNICC model was poorly predictive of microbiologically documented infection (MDI) in these validation cohorts. The pooled AUC-ROC was 0.59, 95% CI 0.41 to 0.78, tau2=0, compared with derivation value of 0.72, 95% CI 0.71 to 0.76. There was poor discrimination (pooled slope estimate 0.03, 95% CI −0.19 to 0.26) and calibration in the large (pooled E/O ratio 1.48, 95% CI 0.87 to 2.1). Three different simple recalibration approaches failed to improve performance meaningfully.ConclusionThis meta-analysis shows the PICNICC model should not be used at admission to predict MDI. Further work should focus on validating alternative prediction models. Validation across multiple cohorts from diverse locations is essential before widespread clinical adoption of such rules to avoid overtreating or undertreating children with fever with neutropenia.

scholarly journals Establishment of a prediction model for early and mid-term complications for patients undergoing catheter insertion for peritoneal dialysis

2021 ◽  
Vol 49 (4) ◽  
pp. 030006052110045
Author(s):  
Yibo Ma ◽  
Shuiqing Liu ◽  
Min Yang ◽  
Yun Zou ◽  
Dong Xue ◽  
...  
Keyword(s):  
Peritoneal Dialysis ◽  
Confidence Interval ◽  
Prediction Model ◽  
Prediction Models ◽  
Multivariate Logistic Regression Analysis ◽  
Area Under The Curve ◽  
General Information ◽  
Catheter Insertion ◽  
Early Complications ◽  
Roc Curve Analysis

Objective To investigate the factors involved in early and mid-term complications after catheter insertion for peritoneal dialysis and to establish prediction models. Methods A total of 158 patients with peritoneal dialysis in the Department of Nephrology of our hospital were retrospectively analyzed. General information, laboratory indices, early complications (within 1 month after the operation), mid-term complications (1–6 months after the operation), and other relevant data were recorded. Multivariate logistic regression analysis was performed to establish a prediction model of complications and generate a nomogram. Receiver operating characteristic (ROC) curve analysis was used to evaluate the efficacy of the model. Results Among the patients, 48 (30.8%) had early complications, which were mainly catheter-related complications, and 29 (18.4%) had mid-term complications, which were mainly abdominal infection and catheter migration. We constructed a prediction model for early complications (area under the curve = 0.697, 95% confidence interval: 0.609–0.785) and mid-term complications (area under the curve = 0.730, 95% confidence interval: 0.622–0.839). The sensitivity was 0.750 and 0.607, and the specificity was 0.589 and 0.765, respectively. Conclusions Our prediction model has clinical significance for risk assessment of early and mid-term complications and prevention of complications after catheterization for peritoneal dialysis.

scholarly journals Prediction Scores for Acute Kidney Injury following Adult Cardiac Surgery

2020 ◽  
Author(s):  
Yu Tian ◽  
Wei Zhao ◽  
Yuefu Wang ◽  
Chunrong Wang ◽  
Xiaolin Diao ◽  
...  
Keyword(s):  
Acute Kidney Injury ◽  
Cardiac Surgery ◽  
Prediction Model ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Kidney Injury ◽  
Scoring Systems ◽  
Multivariable Logistic Regression Analysis ◽  
Derivation Cohort ◽  
Associated Risk Factors

Abstract Background In the development of scoring systems for acute kidney injury (AKI) following cardiac surgery, previous investigations have primarily and solely attached importance to preoperative associated risk factors without any consideration for surgery-derived physiopathology. We sought to internally derive and then validate risk score systems using pre- and intraoperative variables to predict the occurrence of any-stage (stage 1-3) and stage-3 AKI within 7 days.Methods Patients undergoing cardiac surgery from Jan 1, 2012, to Jan 1, 2019, were enrolled in our retrospective study. The clinical data were divided into a derivation cohort (n= 43799) and a validation cohort (n= 14600). Multivariable logistic regression analysis was used to develop the prediction models.Results The overall prevalence of any-stage and stage-3 AKI after cardiac surgery was 34.3% and 1.7%, respectively. Any-stage AKI prediction-model discrimination measured by the area under the curve (AUC) was acceptable (AUC = 0.69, 95% CI: 0.68, 0.69), and the prediction model calibration measured by the Hosmer-Lemshow test was good (P = 0.95). The stage-3 AKI prediction model had an AUC of 0.84 (95% CI 0.83, 0.85) and good calibration according to the Hosmer-Lemshow test (P = 0.73).Conclusions Using pre- and intraoperative data, we developed two scoring systems for any-stage AKI and stage-3 AKI in a cardiac surgery population. These scoring systems can potentially be adopted clinically in the field of AKI recognition and therapeutic intervention.

scholarly journals Prediction of remission in pharmacotherapy of untreated major depression: development and validation of multivariable prediction models

2018 ◽  
Vol 49 (14) ◽  
pp. 2405-2413 ◽  
Author(s):  
Toshi A. Furukawa ◽  
Tadashi Kato ◽  
Yoshihiro Shinagawa ◽  
Kazuhira Miki ◽  
Hirokazu Fujita ◽  
...  
Keyword(s):  
Major Depression ◽  
Prediction Model ◽  
Goodness Of Fit ◽  
Prediction Models ◽  
Secondary Analysis ◽  
Area Under The Curve ◽  
Stepwise Logistic Regression ◽  
Patient Health ◽  
Validation Set ◽  
Using Data

AbstractBackgroundDepression is increasingly recognized as a chronic and relapsing disorder. However, an important minority of patients who start treatment for their major depressive episode recover to euthymia. It is clinically important to be able to predict such individuals.MethodsThe study is a secondary analysis of a recently completed pragmatic megatrial examining first- and second-line treatments for hitherto untreated episodes of non-psychotic unipolar major depression (n = 2011). Using the first half of the cohort as the derivation set, we applied multiply-imputed stepwise logistic regression with backward selection to build a prediction model to predict remission, defined as scoring 4 or less on the Patient Health Quetionnaire-9 at week 9. We used three successively richer sets of predictors at baseline only, up to week 1, and up to week 3. We examined the external validity of the derived prediction models with the second half of the cohort.ResultsIn total, 37.0% (95% confidence interval 34.8–39.1%) were in remission at week 9. Only the models using data up to week 1 or 3 showed reasonable performance. Age, education, length of episode and depression severity remained in the multivariable prediction models. In the validation set, the discrimination of the prediction model was satisfactory with the area under the curve of 0.73 (0.70–0.77) and 0.82 (0.79–0.85), while the calibration was excellent with non-significant goodness-of-fit χ2 values (p = 0.41 and p = 0.29), respectively.ConclusionsPatients and clinicians can use these prediction models to estimate their predicted probability of achieving remission after acute antidepressant therapy.

External Validation of Three Prediction Tools for Patients at Risk of a Complicated Course of Clostridium difficile Infection: Disappointing in an Outbreak Setting

2017 ◽  
Vol 38 (8) ◽  
pp. 897-905 ◽  
Author(s):  
Yvette H. van Beurden ◽  
Marjolein P. M. Hensgens ◽  
Olaf M. Dekkers ◽  
Saskia Le Cessie ◽  
Chris J. J. Mulder ◽  
...  
Keyword(s):  
Clostridium Difficile ◽  
Prediction Model ◽  
Model Calibration ◽  
Prediction Models ◽  
Validation Cohort ◽  
External Validation ◽  
Area Under The Curve ◽  
Patients At Risk ◽  
Complicated Course ◽  
Welfare Model

OBJECTIVEEstimating the risk of a complicated course of Clostridium difficile infection (CDI) might help doctors guide treatment. We aimed to validate 3 published prediction models: Hensgens (2014), Na (2015), and Welfare (2011).METHODSThe validation cohort comprised 148 patients diagnosed with CDI between May 2013 and March 2014. During this period, 70 endemic cases of CDI occurred as well as 78 cases of CDI related to an outbreak of C. difficile ribotype 027. Model calibration and discrimination were assessed for the 3 prediction rules.RESULTSA complicated course (ie, death, colectomy, or ICU admission due to CDI) was observed in 31 patients (21%), and 23 patients (16%) died within 30 days of CDI diagnosis. The performance of all 3 prediction models was poor when applied to the total validation cohort with an estimated area under the curve (AUC) of 0.68 for the Hensgens model, 0.54 for the Na model, and 0.61 for the Welfare model. For those patients diagnosed with CDI due to non-outbreak strains, the prediction model developed by Hensgens performed the best, with an AUC of 0.78.CONCLUSIONAll 3 prediction models performed poorly when using our total cohort, which included CDI cases from an outbreak as well as endemic cases. The prediction model of Hensgens performed relatively well for patients diagnosed with CDI due to non-outbreak strains, and this model may be useful in endemic settings.Infect Control Hosp Epidemiol 2017;38:897–905

scholarly journals Study protocol: Comparison of different risk prediction modelling approaches for COVID-19 related death using the OpenSAFELY platform

2020 ◽  
Vol 5 ◽  
pp. 243 ◽  
Author(s):  
◽  
Elizabeth J. Williamson ◽  
John Tazare ◽  
Krishnan Bhaskaran ◽  
Alex J Walker ◽  
...  
Keyword(s):  
Prediction Model ◽  
Risk Prediction ◽  
Prediction Models ◽  
Large Population ◽  
World Health ◽  
Collective Good ◽  
Infection Prevalence ◽  
Time Varying ◽  
Over Time ◽  
Modelling Approaches

On March 11th 2020, the World Health Organization characterised COVID-19 as a pandemic. Responses to containing the spread of the virus have relied heavily on policies involving restricting contact between people. Evolving policies regarding shielding and individual choices about restricting social contact will rely heavily on perceived risk of poor outcomes from COVID-19. In order to make informed decisions, both individual and collective, good predictive models are required.   For outcomes related to an infectious disease, the performance of any risk prediction model will depend heavily on the underlying prevalence of infection in the population of interest. Incorporating measures of how this changes over time may result in important improvements in prediction model performance.  This protocol reports details of a planned study to explore the extent to which incorporating time-varying measures of infection burden over time improves the quality of risk prediction models for COVID-19 death in a large population of adult patients in England. To achieve this aim, we will compare the performance of different modelling approaches to risk prediction, including static cohort approaches typically used in chronic disease settings and landmarking approaches incorporating time-varying measures of infection prevalence and policy change, using COVID-19 related deaths data linked to longitudinal primary care electronic health records data within the OpenSAFELY secure analytics platform.

scholarly journals Factors affecting the performance of brain arteriovenous malformation rupture prediction models

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Wengui Tao ◽  
Langchao Yan ◽  
Ming Zeng ◽  
Fenghua Chen
Keyword(s):  
Arteriovenous Malformation ◽  
Prediction Model ◽  
Clinical Data ◽  
Prediction Models ◽  
Area Under The Curve ◽  
Training Dataset ◽  
Sample Sizes ◽  
Risk Of Rupture ◽  
Risk Predictors ◽  
Sampling Times

Abstract Background In many cases, both the rupture rate of cerebral arteriovenous malformation (bAVM) in patients and the risk of endovascular or surgical treatment (when radiosurgery is not appropriate) are not low, it is important to assess the risk of rupture more cautiously before treatment. Based on the current high-risk predictors and clinical data, different sample sizes, sampling times and algorithms were used to build prediction models for the risk of hemorrhage in bAVM, and the accuracy and stability of the models were investigated. Our purpose was to remind researchers that there may be some pitfalls in developing similar prediction models. Methods The clinical data of 353 patients with bAVMs were collected. During the creation of prediction models for bAVM rupture, we changed the ratio of the training dataset to the test dataset, increased the number of sampling times, and built models for predicting bAVM rupture by the logistic regression (LR) algorithm and random forest (RF) algorithm. The area under the curve (AUC) was used to evaluate the predictive performances of those models. Results The performances of the prediction models built by both algorithms were not ideal (AUCs: 0.7 or less). The AUCs from the models built by the LR algorithm with different sample sizes were better than those built by the RF algorithm (0.70 vs 0.68, p < 0.001). The standard deviations (SDs) of the AUCs from both prediction models with different sample sizes displayed wide ranges (max range > 0.1). Conclusions Based on the current risk predictors, it may be difficult to build a stable and accurate prediction model for the hemorrhagic risk of bAVMs. Compared with sample size and algorithms, meaningful predictors are more important in establishing an accurate and stable prediction model.

Stigmatisation of mental illness in the workplace: evidence and consequences

2013 ◽  
Vol 10 (02) ◽  
pp. 102-107 ◽  
Author(s):  
N. Bezborodovs ◽  
G. Thornicroft
Keyword(s):  
Mental Health ◽  
Mental Illness ◽  
Everyday Life ◽  
Mental Health Problems ◽  
Self Esteem ◽  
Health Problems ◽  
Work Place ◽  
Stigma And Discrimination ◽  
Workplace Stigma

SummaryWork plays an important part in everyday life. For people experiencing mental health problems employment may both provide a source of income, improved self-esteem and stability, and influence the course and outcomes of the disorder. Yet in many countries the work-place consistently surfaces as the context where people with mental health problems feel stigmatised and discriminated the most. This paper will review the existing evidence of stigma and discrimination in the workplace, consider the consequences of workplace stigma on the lives of people experiencing mental health problems, and discuss implications for further action.

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