scholarly journals Development and performance evaluation of the Medicines Optimisation Assessment Tool (MOAT): a prognostic model to target hospital pharmacists’ input to prevent medication-related problems

2019 ◽  
Vol 28 (8) ◽  
pp. 645-656 ◽  
Author(s):  
Cathy Geeson ◽  
Li Wei ◽  
Bryony Dean Franklin
Keyword(s):  
Risk Factors ◽  
Assessment Tool ◽  
Predictive Accuracy ◽  
External Validation ◽  
Predictive Performance ◽  
Study Data ◽  
Predictive Values ◽  
Hospital Pharmacists ◽  
Potential Risk Factors ◽  
Medicines Optimisation

BackgroundMedicines optimisation is a key role for hospital pharmacists, but with ever-increasing demands on services, there is a need to increase efficiency while maintaining patient safety.ObjectiveTo develop a prediction tool, the Medicines Optimisation Assessment Tool (MOAT), to target patients most in need of pharmacists’ input in hospital.MethodsPatients from adult medical wards at two UK hospitals were prospectively included into this cohort study. Data on medication-related problems (MRPs) were collected by pharmacists at the study sites as part of their routine daily clinical assessments. Data on potential risk factors, such as number of comorbidities and use of ‘high-risk’ medicines, were collected retrospectively. Multivariable logistic regression modelling was used to determine the relationship between risk factors and the study outcome: preventable MRPs that were at least moderate in severity. The model was internally validated and a simplified electronic scoring system developed.ResultsAmong 1503 eligible admissions, 610 (40.6%) experienced the study outcome. Eighteen risk factors were preselected for MOAT development, with 11 variables retained in the final model. The MOAT demonstrated fair predictive performance (concordance index 0.66) and good calibration. Two clinically relevant decision thresholds (ie, the minimum predicted risk probabilities to justify pharmacists’ input) were selected, with sensitivities of 90% and 66% (specificity 30% and 61%); these equate to positive predictive values of 47% and 54%, respectively. Decision curve analysis suggests that the MOAT has potential value in clinical practice in guiding decision-making.ConclusionThe MOAT has potential to predict those patients most at risk of moderate or severe preventable MRPs, experienced by 41% of admissions. External validation is now required to establish predictive accuracy in a new group of patients.

scholarly journals Medicines Optimisation Assessment Tool (MOAT): a prognostic model to target hospital pharmacists' input to improve patient outcomes. Protocol for an observational study

BMJ Open ◽  
2017 ◽  
Vol 7 (6) ◽  
pp. e017509 ◽  
Author(s):  
Cathy Geeson ◽  
Li Wei ◽  
Bryony Dean Franklin
Keyword(s):  
Risk Factors ◽  
Potential Risk ◽  
Prognostic Model ◽  
Assessment Tool ◽  
Research Strategy ◽  
Service Research ◽  
Hospital Pharmacists ◽  
Potential Risk Factors ◽  
Medicines Optimisation ◽  
Study Sites

IntroductionMedicines optimisation is a key role for hospital pharmacists, but with ever-increasing demands on services there is a need to increase efficiency while maintaining patient safety. The aim of this study is to develop a prognostic model, the Medicines Optimisation Assessment Tool (MOAT), which can be used to target patients most in need of pharmacists' input while in hospital.Methods and analysisThe MOAT will be developed following recommendations of the Prognosis Research Strategy partnership. Using a cohort study we will prospectively include 1500 adult patients from the medical wards of two UK hospitals. Data on medication-related problems (MRPs) experienced by study patients will be collected by pharmacists at the study sites as part of their routine daily clinical assessment of patients. Data on potential risk factors such as polypharmacy, renal impairment and the use of 'high risk' medicines will be collected retrospectively from the information departments at the study sites, laboratory reporting systems and patient medical records. Multivariable logistic regression models will then be used to determine the relationship between potential risk factors and the study outcome of preventable MRPs that are at least moderate in severity. Bootstrapping will be used to adjust the MOAT for optimism, and predictive performance will be assessed using calibration and discrimination. A simplified scoring system will also be developed, which will be assessed for sensitivity and specificity.Ethics and disseminationThis study has been approved by the Proportionate Review Service Sub-Committee of the National Health Service Research Ethics Committee Wales REC 7 (16/WA/0016) and the Health Research Authority (project ID 197298). We plan to disseminate the results via presentations at relevant patient/public, professional, academic and scientific meetings and conferences, and will submit findings for publication in peer-reviewed journals.Trial registration numberNCT02582463.

A proof-of-concept study applying machine learning methods to putative risk factors for eating disorders: results from the multi-centre European project on healthy eating

2021 ◽  
pp. 1-10
Author(s):  
I. Krug ◽  
J. Linardon ◽  
C. Greenwood ◽  
G. Youssef ◽  
J. Treasure ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Logistic Regression ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Prediction Rule ◽  
Predictive Performance ◽  
Individual Risk ◽  
European Project ◽  
Wide Range

Abstract Background Despite a wide range of proposed risk factors and theoretical models, prediction of eating disorder (ED) onset remains poor. This study undertook the first comparison of two machine learning (ML) approaches [penalised logistic regression (LASSO), and prediction rule ensembles (PREs)] to conventional logistic regression (LR) models to enhance prediction of ED onset and differential ED diagnoses from a range of putative risk factors. Method Data were part of a European Project and comprised 1402 participants, 642 ED patients [52% with anorexia nervosa (AN) and 40% with bulimia nervosa (BN)] and 760 controls. The Cross-Cultural Risk Factor Questionnaire, which assesses retrospectively a range of sociocultural and psychological ED risk factors occurring before the age of 12 years (46 predictors in total), was used. Results All three statistical approaches had satisfactory model accuracy, with an average area under the curve (AUC) of 86% for predicting ED onset and 70% for predicting AN v. BN. Predictive performance was greatest for the two regression methods (LR and LASSO), although the PRE technique relied on fewer predictors with comparable accuracy. The individual risk factors differed depending on the outcome classification (EDs v. non-EDs and AN v. BN). Conclusions Even though the conventional LR performed comparably to the ML approaches in terms of predictive accuracy, the ML methods produced more parsimonious predictive models. ML approaches offer a viable way to modify screening practices for ED risk that balance accuracy against participant burden.

scholarly journals Clinical Utility of a Nomogram for Predicting 30-Days Poor Outcome in Hospitalized Patients With COVID-19: Multicenter External Validation and Decision Curve Analysis

2020 ◽  
Vol 7 ◽  
Author(s):  
Bin Zhang ◽  
Qin Liu ◽  
Xiao Zhang ◽  
Shuyi Liu ◽  
Weiqi Chen ◽  
...  
Keyword(s):  
Prognostic Factors ◽  
Clinical Utility ◽  
Predictive Accuracy ◽  
Fasting Blood Glucose ◽  
External Validation ◽  
Predictive Performance ◽  
Curve Analysis ◽  
Discriminative Ability ◽  
Decision Curve Analysis ◽  
Poor Outcome

Aim: Early detection of coronavirus disease 2019 (COVID-19) patients who are likely to develop worse outcomes is of great importance, which may help select patients at risk of rapid deterioration who should require high-level monitoring and more aggressive treatment. We aimed to develop and validate a nomogram for predicting 30-days poor outcome of patients with COVID-19.Methods: The prediction model was developed in a primary cohort consisting of 233 patients with laboratory-confirmed COVID-19, and data were collected from January 3 to March 20, 2020. We identified and integrated significant prognostic factors for 30-days poor outcome to construct a nomogram. The model was subjected to internal validation and to external validation with two separate cohorts of 110 and 118 cases, respectively. The performance of the nomogram was assessed with respect to its predictive accuracy, discriminative ability, and clinical usefulness.Results: In the primary cohort, the mean age of patients was 55.4 years and 129 (55.4%) were male. Prognostic factors contained in the clinical nomogram were age, lactic dehydrogenase, aspartate aminotransferase, prothrombin time, serum creatinine, serum sodium, fasting blood glucose, and D-dimer. The model was externally validated in two cohorts achieving an AUC of 0.946 and 0.878, sensitivity of 100 and 79%, and specificity of 76.5 and 83.8%, respectively. Although adding CT score to the clinical nomogram (clinical-CT nomogram) did not yield better predictive performance, decision curve analysis showed that the clinical-CT nomogram provided better clinical utility than the clinical nomogram.Conclusions: We established and validated a nomogram that can provide an individual prediction of 30-days poor outcome for COVID-19 patients. This practical prognostic model may help clinicians in decision making and reduce mortality.

scholarly journals Predicting suicide following self-harm: systematic review of risk factors and risk scales

2016 ◽  
Vol 209 (4) ◽  
pp. 277-283 ◽  
Author(s):  
Melissa K. Y. Chan ◽  
Henna Bhatti ◽  
Nick Meader ◽  
Sarah Stockton ◽  
Jonathan Evans ◽  
...  
Keyword(s):  
Risk Factors ◽  
Systematic Review ◽  
Suicide Ideation ◽  
Predictive Accuracy ◽  
Meta Analysis ◽  
Mental Healthcare ◽  
Sufficient Evidence ◽  
Predictive Values ◽  
Self Harm ◽  
The Individual

BackgroundPeople with a history of self-harm are at a far greater risk of suicide than the general population. However, the relationship between self-harm and suicide is complex.AimsTo undertake the first systematic review and meta-analysis of prospective studies of risk factors and risk assessment scales to predict suicide following self-harm.MethodWe conducted a search for prospective cohort studies of populations who had self-harmed. For the review of risk scales we also included studies examining the risk of suicide in people under specialist mental healthcare, in order to broaden the scope of the review and increase the number of studies considered. Differences in predictive accuracy between populations were examined where applicable.ResultsTwelve studies on risk factors and 7 studies on risk scales were included. Four risk factors emerged from the metaanalysis, with robust effect sizes that showed little change when adjusted for important potential confounders. These included: previous episodes of self-harm (hazard ratio (HR) = 1.68, 95% CI 1.38–2.05, K = 4), suicidal intent (HR = 2.7, 95% CI 1.91–3.81, K = 3), physical health problems (HR = 1.99, 95% CI 1.16–3.43, K = 3) and male gender (HR = 2.05, 95% CI 1.70–2.46, K = 5). The included studies evaluated only three risk scales (Beck Hopelessness Scale (BHS), Suicide Intent Scale (SIS) and Scale for Suicide Ideation). Where meta-analyses were possible (BHS, SIS), the analysis was based on sparse data and a high heterogeneity was observed. The positive predictive values ranged from 1.3 to 16.7%.ConclusionsThe four risk factors that emerged, although of interest, are unlikely to be of much practical use because they are comparatively common in clinical populations. No scales have sufficient evidence to support their use. The use of these scales, or an over-reliance on the identification of risk factors in clinical practice, may provide false reassurance and is, therefore, potentially dangerous. Comprehensive psychosocial assessments of the risks and needs that are specific to the individual should be central to the management of people who have self-harmed.

scholarly journals Artificial Neural Networks Versus Multiple Logistic Regression to Predict 30-Day Mortality After Operations For Type A Ascending Aortic Dissection§

2009 ◽  
Vol 3 (1) ◽  
pp. 81-95 ◽  
Author(s):  
Francesco Macrina ◽  
Paolo Emilio Puddu ◽  
Alfonso Sciangula ◽  
Fausto Trigilia ◽  
Marco Totaro ◽  
...  
Keyword(s):  
Risk Factors ◽  
Neural Networks ◽  
Logistic Regression ◽  
Aortic Dissection ◽  
Predictive Accuracy ◽  
Area Under The Curve ◽  
Type A ◽  
Multiple Logistic Regression ◽  
Short Term Mortality ◽  
Potential Risk Factors

Background:There are few comparative reports on the overall accuracy of neural networks (NN), assessed only versus multiple logistic regression (LR), to predict events in cardiovascular surgery studies and none has been performed among acute aortic dissection (AAD) Type A patients.Objectives:We aimed at investigating the predictive potential of 30-day mortality by a large series of risk factors in AAD Type A patients comparing the overall performance of NN versus LR.Methods:We investigated 121 plus 87 AAD Type A patients consecutively operated during 7 years in two Centres. Forced and stepwise NN and LR solutions were obtained and compared, using receiver operating characteristic area under the curve (AUC) and their 95% confidence intervals (CI) and Gini’s coefficients. Both NN and LR models were re-applied to data from the second Centre to adhere to a methodological imperative with NN.Results:Forced LR solutions provided AUC 87.9±4.1% (CI: 80.7 to 93.2%) and 85.7±5.2% (CI: 78.5 to 91.1%) in the first and second Centre, respectively. Stepwise NN solution of the first Centre had AUC 90.5±3.7% (CI: 83.8 to 95.1%). The Gini’s coefficients for LR and NN stepwise solutions of the first Centre were 0.712 and 0.816, respectively. When the LR and NN stepwise solutions were re-applied to the second Centre data, Gini’s coefficients were, respectively, 0.761 and 0.850. Few predictors were selected in common by LR and NN models: the presence of pre-operative shock, intubation and neurological symptoms, immediate post-operative presence of dialysis in continuous and the quantity of post-operative bleeding in the first 24 h. The length of extracorporeal circulation, post-operative chronic renal failure and the year of surgery were specifically detected by NN.Conclusions:Different from the International Registry of AAD, operative and immediate post-operative factors were seen as potential predictors of short-term mortality. We report a higher overall predictive accuracy with NN than with LR. However, the list of potential risk factors to predict 30-day mortality after AAD Type A by NN model is not enlarged significantly.

scholarly journals Integrative Analysis Identified Key Schizophrenia Risk Factors from an Abnormal Behavior Mouse Gene Set

Life ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 172
Author(s):  
Miao Chen ◽  
Weidi Wang ◽  
Weicheng Song ◽  
Wei Qian ◽  
Guan Ning Lin
Keyword(s):  
Risk Factors ◽  
Cell Types ◽  
Brain Regions ◽  
Study Data ◽  
Abnormal Behavior ◽  
Gene Set ◽  
Protein Protein Interaction ◽  
Dorsolateral Prefrontal ◽  
Potential Risk Factors ◽  
Key Genes

Schizophrenia (SCZ) is a severe chronic psychiatric illness with heterogeneous symptoms. However, the pathogenesis of SCZ is unclear, and the number of well-defined SCZ risk factors is limited. We hypothesized that an abnormal behavior (AB) gene set verified by mouse model experiments can be used to better understand SCZ risks. In this work, we carried out an integrative bioinformatics analysis to study two types of risk genes that are either differentially expressed (DEGs) in the case-control study data or carry reported SCZ genetic variants (MUTs). Next, we used RNA-Seq expression data from the hippocampus (HIPPO) and dorsolateral prefrontal cortex (DLPFC) to define the key genes affected by different types (DEGs and MUTs) in different brain regions (DLPFC and HIPPO): DLPFC-kDEG, DLPFC-kMUT, HIPPO-kDEG, and HIPPO-kMUT. The four hub genes (SHANK1, SHANK2, DLG4, and NLGN3) of the biological functionally enriched terms were strongly linked to SCZ via gene co-expression network analysis. Then, we observed that specific spatial expressions of DLPFC-kMUT and HIPPO-kMUT were convergent in the early stages and divergent in the later stages of development. In addition, all four types of key genes showed significantly larger average protein–protein interaction degrees than the background. Comparing the different cell types, the expression of four types of key genes showed specificity in different dimensions. Together, our results offer new insights into potential risk factors and help us understand the complexity and regional heterogeneity of SCZ.

scholarly journals Development and Evaluation of Prognostic Nomogram for Patients with Differentiated Thyroid Carcinoma

2020 ◽  
Author(s):  
Guochao Mao ◽  
Shuai Lin ◽  
Zhangjian Zhou
Keyword(s):  
Thyroid Carcinoma ◽  
Cox Regression ◽  
Validation Cohort ◽  
Predictive Accuracy ◽  
External Validation ◽  
Survival Rates ◽  
Differentiated Thyroid Carcinoma ◽  
Cox Regression Analysis ◽  
Discrimination Ability ◽  
Potential Risk Factors

Abstract Background: Papillary thyroid carcinoma and follicular thyroid carcinoma are both well-differentiated thyroid carcinomas. Here, we aimed to establish and evaluate a nomogram for patients with differentiated thyroid cancer.Methods: Patient records were available from SEER database. We enrolled 17,659 patients in total and randomly separated them into a modeling cohort (n = 12,363, 70%) and a validation cohort (n = 5,296, 30%). Predictive models were established via univariate and multivariate Cox regression analysis of potential risk factors and used to produce a nomogram. Performance of the nomograms in terms of discrimination ability and calibration was evaluated by determining the concordance index (C-index) and by generating calibration plots, respectively, using the internal (modeling cohort) and external (validation cohort) validity.Results: Seven independent prognostic factors (age, race, sex, grade, AJCC T stage, AJCC N stage, and AJCC M stage) were identified and used to develop the nomogram for OS prediction of patients with DTC. The C-index for the modeling cohort was 0.829 (95% CI: 0.807-0.851), and the C-index for the validation cohort was 0.833 (95% CI: 0.803-0.862). Calibration plots of the nomogram indicated acceptable agreement between the predicted 3-, 5-year survival rates and the actual observations in the modeling and validation groups.Conclusions: We have constructed and verified a nomogram containing clinical factors, which showed better prognostic judgment and predictive accuracy for DTC. This will enable clinicians and patients to easily personalize and quantify the probability of DTC during the postoperative period.

scholarly journals Venous Thrombosis Risk after Arthroscopy of the Knee: Derivation and Validation of the L-TRiP(ascopy) Score

2018 ◽  
Vol 118 (10) ◽  
pp. 1823-1831 ◽  
Author(s):  
Banne Nemeth ◽  
Raymond van Adrichem ◽  
Astrid van Hylckama Vlieg ◽  
Trevor Baglin ◽  
Frits Rosendaal ◽  
...  
Keyword(s):  
Risk Factors ◽  
Venous Thrombosis ◽  
Case Control Study ◽  
Knee Arthroscopy ◽  
External Validation ◽  
Case Control ◽  
Complete Model ◽  
Predictive Values ◽  
Thrombosis Risk ◽  
Control Study

AbstractPatients at high risk for venous thrombosis (VT) following knee arthroscopy could potentially benefit from thromboprophylaxis. We explored the predictive values of environmental, genetic risk factors and levels of coagulation markers to integrate these into a prediction model. Using a population-based case–control study into the aetiology of VT, we developed a Complete (all variables), Screening (easy to use in clinical practice) and Clinical (only environmental risk factors) model. The Clinical model was transformed into the Leiden-Thrombosis Risk Prediction (arthroscopy) score [L-TRiP(ascopy) score]. Model validation was performed both internally and externally in another case–control study. A total of 4,943 cases and 6,294 controls were maintained in the analyses, 107 cases and 26 controls had undergone knee arthroscopy. Twelve predictor variables (8 environmental, 3 haemorheological and 1 genetic) were selected from 52 candidates and incorporated into the Complete model (area under the curve [AUC] of 0.81, 95% confidence interval [CI], 0.76–0.86). The Screening model (9 predictors: environmental factors plus factor VIII activity) reached an AUC of 0.76 (95% CI, 0.64–0.88) and the Clinical (and corresponding L-TRiP(ascopy)) model an AUC of 0.72 (95% CI, 0.60–0.83). In the internal and external validation, the Complete model reached an AUC of 0.78 (95% CI, 0.52–0.98) and 0.75 (95% CI, 0.42–1.00), respectively, while the other models performed slightly less well.

scholarly journals Accuracy of individual and combined risk-scale items in the prediction of repetition of self-harm: multicentre prospective cohort study

BJPsych Open ◽  
2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Anna Kathryn Taylor ◽  
Sarah Steeg ◽  
Leah Quinlivan ◽  
David Gunnell ◽  
Keith Hawton ◽  
...  
Keyword(s):  
Risk Factors ◽  
Cohort Study ◽  
Prospective Cohort Study ◽  
Prospective Cohort ◽  
Predictive Accuracy ◽  
Sensitivity Analyses ◽  
Patient Risk ◽  
Predictive Values ◽  
Self Harm ◽  
Risk Scale

Background Individuals attending emergency departments following self-harm have increased risks of future self-harm. Despite the common use of risk scales in self-harm assessment, there is growing evidence that combinations of risk factors do not accurately identify those at greatest risk of further self-harm and suicide. Aims To evaluate and compare predictive accuracy in prediction of repeat self-harm from clinician and patient ratings of risk, individual risk-scale items and a scale constructed with top-performing items. Method We conducted secondary analysis of data from a five-hospital multicentre prospective cohort study of participants referred to psychiatric liaison services following self-harm. We tested predictive utility of items from five risk scales: Manchester Self-Harm Rule, ReACT Self-Harm Rule, SAD PERSONS, Modified SAD PERSONS, Barratt Impulsiveness Scale and clinician and patient risk estimates. Area under the curve (AUC), sensitivity, specificity, predictive values and likelihood ratios were used to evaluate predictive accuracy, with sensitivity analyses using classification-tree regression. Results A total of 483 self-harm episodes were included, and 145 (30%) were followed by a repeat presentation within 6 months. AUC of individual items ranged from 0.43–0.65. Combining best performing items resulted in an AUC of 0.56. Some individual items outperformed the scale they originated from; no items were superior to clinician or patient risk estimations. Conclusions No individual or combination of items outperformed patients’ or clinicians’ ratings. This suggests there are limitations to combining risk factors to predict risk of self-harm repetition. Risk scales should have little role in the management of people who have self-harmed.

Microbleeds after Stent-assisted Coil Embolization of Unruptured Intracranial Aneurysms: Incidence, Risk Factors and the Role of Thromboelastography

2020 ◽  
Vol 17 (4) ◽  
pp. 502-509
Author(s):  
Rui Xu ◽  
Chongjie Cheng ◽  
Yue Wu ◽  
Zongduo Guo ◽  
Xiaochuan Sun ◽  
...  
Keyword(s):  
Risk Factors ◽  
Platelet Function ◽  
Intracranial Aneurysms ◽  
Maximum Amplitude ◽  
Adenosine Diphosphate ◽  
Receiver Operating Curve ◽  
Predictive Values ◽  
Unruptured Intracranial Aneurysms ◽  
Potential Risk Factors ◽  
R Value

Objective: To analyze the incidence and risk factors of microbleeds lesions and to use thromboelastography (TEG) to evaluate the relationship between perioperative platelet function and microbleed events in patients with unruptured intracranial aneurysms (UIAs) undergoing Stent-Assisted Coil (SAC) embolization. Methods: We retrospectively enrolled 261 patients with UIAs undergoing SAC embolization between November 2017 and October 2019. All patients received unanimous antiplatelet protocol (aspirin 300 mg and clopidogrel 300 mg). Platelet function was evaluated by TEG, and magnetic resonance susceptibility-weighted imaging (SWI) was performed for microbleeds detection before and after surgery. Univariate and multivariate logistic regression analyses were used to identify potential risk factors for microbleeds following embolization. Results: Microbleed lesions were identified in 122 of 261 patients (46.7%). Most of the microbleeds were asymptomatic, except for 22 patients complaining slight headaches, and 3 patients who developed cerebral hemorrhage after discharge. Among the clinical characters, female, previous intracerebral hemorrhage (ICH) history and TEG parameters variation (higher reaction time (R) and lower maximum amplitude of adenosine diphosphate (MAADP)) were associated with microbleeds occurrence. Subsequent multivariate analysis indicated that gender, hemorrhage history, R, and MAADP were still independent risk factors of microbleeds. The R-value (>7.6 min) and MAADP (<29.2 mm) were predictive values, yielding areas under the receiver operating curve (ROC) of 0.76 (95% CI 0.70 to 0.82) and 0.89 (95% CI 0.86 to 0.93), respectively. Conclusion: The incidence of microbleeds may be high in UIA patients treated with SAC and dual antiplatelet therapy. Lesions occurred more frequently in female patients and patients with ICH history. Among the TEG parameters, the R-value and MAADP were predictors for microbleed events.

Sign in / Sign up

Export Citation Format

Share Document