scholarly journals Screening Tools Used by Clinical Pharmacists to Identify Elderly Patients at Risk of Drug-Related Problems on Hospital Admission: A Systematic Review

Pharmacy ◽  
2020 ◽  
Vol 8 (2) ◽  
pp. 64 ◽  
Author(s):  
Amanda Brady ◽  
Chris E. Curtis ◽  
Zahraa Jalal
Keyword(s):  
Systematic Review ◽  
Elderly Patients ◽  
Risk Prediction ◽  
Risk Score ◽  
Prediction Models ◽  
External Validation ◽  
Screening Tools ◽  
Cochrane Library ◽  
Drug Related Problems ◽  
Increased Risk

In recent years, a number of studies have examined tools to identify elderly patients who are at increased risk of drug-related problems (DRPs). There has been interest in developing tools to prioritise patients for clinical pharmacist (CP) review. This systematic review (SR) aimed to identify published primary research in this area and critically evaluate the quality of prediction tools to identify elderly patients at increased risk of DRPs and/or likely to need CP intervention. The PubMed, EMBASE, OVID HMIC, Cochrane Library, PsychInfo, CINAHL PLUS, Web of Science and ProQuest databases were searched. Keeping up to date with research and citations, the reference lists of included articles were also searched to identify relevant studies. The studies involved the development, utilisation and/or validation of a prediction tool. The protocol for this SR, CRD42019115673, was registered on PROSPERO. Data were extracted and systematically assessed for quality by considering the four key stages involved in accurate risk prediction models—development, validation, impact and implementation—and following the Checklist for the critical Appraisal and data extraction for systematic Reviews of prediction Modelling Studies (CHARMS). Nineteen studies met the inclusion criteria. Variations in study design, participant characteristics and outcomes made meta-analysis unsuitable. The tools varied in complexity. Most studies reported the sensitivity, specificity and/or discriminatory ability of the tool. Only four studies included external validation of the tool(s), namely of the BADRI model and the GerontoNet ADR Risk Score. The BADRI score demonstrated acceptable goodness of fit and good discrimination performance, whilst the GerontoNet ADR Risk Score showed poor reliability in external validation. None of the models met the four key stages required to create a quality risk prediction model. Further research is needed to either refine the tools developed to date or develop new ones that have good performance and have been externally validated before considering the potential impact and implementation of such tools.

scholarly journals Risk Prediction Models for Patients With Acute Heart Failure: A Protocol for Systematic Review, Critical Appraisal, and Meta-Analysis

2021 ◽  
Author(s):  
Xuecheng Zhang ◽  
Kehua Zhou ◽  
Jingjing Zhang ◽  
Ying Chen ◽  
Hengheng Dai ◽  
...  
Keyword(s):  
Heart Failure ◽  
Systematic Review ◽  
Prediction Model ◽  
Acute Heart Failure ◽  
Risk Prediction ◽  
Methodological Quality ◽  
Critical Appraisal ◽  
Prediction Models ◽  
Cochrane Library ◽  
Risk Prediction Models

Abstract Background Nearly a third of patients with acute heart failure (AHF) die or are readmitted within three months after discharge, accounting for the majority of costs associated with heart failure-related care. A considerable number of risk prediction models, which predict outcomes for mortality and readmission rates, have been developed and validated for patients with AHF. These models could help clinicians stratify patients by risk level and improve decision making, and provide specialist care and resources directed to high-risk patients. However, clinicians sometimes reluctant to utilize these models, possibly due to their poor reliability, the variety of models, and/or the complexity of statistical methodologies. Here, we describe a protocol to systematically review extant risk prediction models. We will describe characteristics, compare performance, and critically appraise the reporting transparency and methodological quality of risk prediction models for AHF patients. Method Embase, Pubmed, Web of Science, and the Cochrane Library will be searched from their inception onwards. A back word will be searched on derivation studies to find relevant external validation studies. Multivariable prognostic models used for AHF and mortality and/or readmission rate will be eligible for review. Two reviewers will conduct title and abstract screening, full-text review, and data extraction independently. Included models will be summarized qualitatively and quantitatively. We will also provide an overview of critical appraisal of the methodological quality and reporting transparency of included studies using the Prediction model Risk of Bias Assessment Tool(PROBAST tool) and the Transparent Reporting of a multivariable prediction model for Individual Prognosis or Diagnosis(TRIPOD statement). Discussion The result of the systematic review could help clinicians better understand and use the prediction models for AHF patients, as well as make standardized decisions about more precise, risk-adjusted management. Systematic review registration : PROSPERO registration number CRD42021256416.

P–419 On prognosis after unexplained recurrent pregnancy losses (RPL); a systematic review and external validation of clinical prediction models

2021 ◽  
Vol 36 (Supplement_1) ◽  
Author(s):  
A Youssef
Keyword(s):  
Systematic Review ◽  
Supportive Care ◽  
Prediction Model ◽  
Pregnancy Outcome ◽  
Prediction Models ◽  
External Validation ◽  
Model Development ◽  
Predictive Performance ◽  
Pregnancy Rates ◽  
Cochrane Library

Abstract Study question Which models that predict pregnancy outcome in couples with unexplained RPL exist and what is the performance of the most used model? Summary answer We identified seven prediction models; none followed the recommended prediction model development steps. Moreover, the most used model showed poor predictive performance. What is known already RPL remains unexplained in 50–75% of couples For these couples, there is no effective treatment option and clinical management rests on supportive care. Essential part of supportive care consists of counselling on the prognosis of subsequent pregnancies. Indeed, multiple prediction models exist, however the quality and validity of these models varies. In addition, the prediction model developed by Brigham et al is the most widely used model, but has never been externally validated. Study design, size, duration We performed a systematic review to identify prediction models for pregnancy outcome after unexplained RPL. In addition we performed an external validation of the Brigham model in a retrospective cohort, consisting of 668 couples with unexplained RPL that visited our RPL clinic between 2004 and 2019. Participants/materials, setting, methods A systematic search was performed in December 2020 in Pubmed, Embase, Web of Science and Cochrane library to identify relevant studies. Eligible studies were selected and assessed according to the TRIPOD) guidelines, covering topics on model performance and validation statement. The performance of predicting live birth in the Brigham model was evaluated through calibration and discrimination, in which the observed pregnancy rates were compared to the predicted pregnancy rates. Main results and the role of chance Seven models were compared and assessed according to the TRIPOD statement. This resulted in two studies of low, three of moderate and two of above average reporting quality. These studies did not follow the recommended steps for model development and did not calculate a sample size. Furthermore, the predictive performance of neither of these models was internally- or externally validated. We performed an external validation of Brigham model. Calibration showed overestimation of the model and too extreme predictions, with a negative calibration intercept of –0.52 (CI 95% –0.68 – –0.36), with a calibration slope of 0.39 (CI 95% 0.07 – 0.71). The discriminative ability of the model was very low with a concordance statistic of 0.55 (CI 95% 0.50 – 0.59). Limitations, reasons for caution None of the studies are specifically named prediction models, therefore models may have been missed in the selection process. The external validation cohort used a retrospective design, in which only the first pregnancy after intake was registered. Follow-up time was not limited, which is important in counselling unexplained RPL couples. Wider implications of the findings: Currently, there are no suitable models that predict on pregnancy outcome after RPL. Moreover, we are in need of a model with several variables such that prognosis is individualized, and factors from both the female as the male to enable a couple specific prognosis. Trial registration number Not applicable

scholarly journals Systematic review of risk prediction scores for surgical site infection or periprosthetic joint infection following joint arthroplasty

2017 ◽  
Vol 145 (9) ◽  
pp. 1738-1749 ◽  
Author(s):  
S. K. KUNUTSOR ◽  
M. R. WHITEHOUSE ◽  
A. W. BLOM ◽  
A. D. BESWICK
Keyword(s):  
Systematic Review ◽  
Risk Prediction ◽  
Prediction Models ◽  
Joint Infection ◽  
External Validation ◽  
Surgical Site Infections ◽  
Predictive Performance ◽  
Risk Scores ◽  
Accurate Identification ◽  
Discriminative Ability

SUMMARYAccurate identification of individuals at high risk of surgical site infections (SSIs) or periprosthetic joint infections (PJIs) influences clinical decisions and development of preventive strategies. We aimed to determine progress in the development and validation of risk prediction models for SSI or PJI using a systematic review. We searched for studies that have developed or validated a risk prediction tool for SSI or PJI following joint replacement in MEDLINE, EMBASE, Web of Science and Cochrane databases; trial registers and reference lists of studies up to September 2016. Nine studies describing 16 risk scores for SSI or PJI were identified. The number of component variables in a risk score ranged from 4 to 45. The C-index ranged from 0·56 to 0·74, with only three risk scores reporting a discriminative ability of >0·70. Five risk scores were validated internally. The National Healthcare Safety Network SSIs risk models for hip and knee arthroplasties (HPRO and KPRO) were the only scores to be externally validated. Except for HPRO which shows some promise for use in a clinical setting (based on predictive performance and external validation), none of the identified risk scores can be considered ready for use. Further research is urgently warranted within the field.

scholarly journals Lung cancer in symptomatic patients presenting in primary care: a systematic review of risk prediction tools

2017 ◽  
Vol 67 (659) ◽  
pp. e396-e404 ◽  
Author(s):  
Mia Schmidt-Hansen ◽  
Sabine Berendse ◽  
Willie Hamilton ◽  
David R Baldwin
Keyword(s):  
Lung Cancer ◽  
Systematic Review ◽  
Primary Care ◽  
Risk Prediction ◽  
External Validation ◽  
Clinical Decision ◽  
Cochrane Library ◽  
Specialist Care ◽  
Prediction Tools ◽  
Primary Care Data

BackgroundLung cancer is the leading cause of cancer deaths. Around 70% of patients first presenting to specialist care have advanced disease, at which point current treatments have little effect on survival. The issue for primary care is how to recognise patients earlier and investigate appropriately. This requires an assessment of the risk of lung cancer.AimThe aim of this study was to systematically review the existing risk prediction tools for patients presenting in primary care with symptoms that may indicate lung cancerDesign and settingSystematic review of primary care data.MethodMedline, PreMedline, Embase, the Cochrane Library, Web of Science, and ISI Proceedings (1980 to March 2016) were searched. The final list of included studies was agreed between two of the authors, who also appraised and summarised them.ResultsSeven studies with between 1482 and 2 406 127 patients were included. The tools were all based on UK primary care data, but differed in complexity of development, number/type of variables examined/included, and outcome time frame. There were four multivariable tools with internal validation area under the curves between 0.88 and 0.92. The tools all had a number of limitations, and none have been externally validated, or had their clinical and cost impact examined.ConclusionThere is insufficient evidence for the recommendation of any one of the available risk prediction tools. However, some multivariable tools showed promising discrimination. What is needed to guide clinical practice is both external validation of the existing tools and a comparative study, so that the best tools can be incorporated into clinical decision tools used in primary care.

scholarly journals Predicting the risk of stroke among patients with type 2 diabetes: a systematic review and meta-analysis of C-statistics

BMJ Open ◽  
2019 ◽  
Vol 9 (8) ◽  
pp. e025579 ◽  
Author(s):  
Mohammad Ziaul Islam Chowdhury ◽  
Fahmida Yeasmin ◽  
Doreen M Rabi ◽  
Paul E Ronksley ◽  
Tanvir C Turin
Keyword(s):  
Systematic Review ◽  
Prediction Models ◽  
Data Extraction ◽  
Meta Analysis ◽  
External Validation ◽  
Predictive Performance ◽  
Eligibility Criteria ◽  
C Statistic ◽  
Increased Risk ◽  
Patients With Diabetes

ObjectiveStroke is a major cause of disability and death worldwide. People with diabetes are at a twofold to fivefold increased risk for stroke compared with people without diabetes. This study systematically reviews the literature on available stroke prediction models specifically developed or validated in patients with diabetes and assesses their predictive performance through meta-analysis.DesignSystematic review and meta-analysis.Data sourcesA detailed search was performed in MEDLINE, PubMed and EMBASE (from inception to 22 April 2019) to identify studies describing stroke prediction models.Eligibility criteriaAll studies that developed stroke prediction models in populations with diabetes were included.Data extraction and synthesisTwo reviewers independently identified eligible articles and extracted data. Random effects meta-analysis was used to obtain a pooled C-statistic.ResultsOur search retrieved 26 202 relevant papers and finally yielded 38 stroke prediction models, of which 34 were specifically developed for patients with diabetes and 4 were developed in general populations but validated in patients with diabetes. Among the models developed in those with diabetes, 9 reported their outcome as stroke, 23 reported their outcome as composite cardiovascular disease (CVD) where stroke was a component of the outcome and 2 did not report stroke initially as their outcome but later were validated for stroke as the outcome in other studies. C-statistics varied from 0.60 to 0.92 with a median C-statistic of 0.71 (for stroke as the outcome) and 0.70 (for stroke as part of a composite CVD outcome). Seventeen models were externally validated in diabetes populations with a pooled C-statistic of 0.68.ConclusionsOverall, the performance of these diabetes-specific stroke prediction models was not satisfactory. Research is needed to identify and incorporate new risk factors into the model to improve models’ predictive ability and further external validation of the existing models in diverse population to improve generalisability.

scholarly journals 702 Evaluation of Risk Prediction Models for Postoperative Pulmonary Complications in Adults Undergoing Major Abdominal Surgery: A Systematic Review and External Validation Study in The Respiratory Complications After Abdominal Surgery (RECON) Cohort

2021 ◽  
Vol 108 (Supplement_2) ◽  
Author(s):  
Keyword(s):  
Systematic Review ◽  
Abdominal Surgery ◽  
Risk Prediction ◽  
Prediction Models ◽  
External Validation ◽  
Pulmonary Complications ◽  
Major Abdominal Surgery ◽  
Postoperative Pulmonary Complications ◽  
Risk Prediction Models ◽  
Prediction Tools

Abstract Introduction Postoperative pulmonary complications (PPCs) following major abdominal surgery result in substantial morbidity and mortality, yet stratifying patients for risk-modifying interventions remains challenging. This study aimed to identify and externally validate PPC risk prediction models in an international, prospective cohort. Method A systematic review was conducted to identify risk prediction models for PPC following abdominal surgery. External validation was performed using data from a prospective dataset of adult patients undergoing major abdominal surgery from January to April 2019 in the UK, Ireland, and Australia. The primary outcome was identification of PPC within 30-days (StEP-COMPAC criteria definition). Model discrimination and diagnostic accuracy were compared. Results Six unique risk prediction models were eligible from 2819 records (112 full texts). These were validated across 11,591 patients, with an overall PPC rate of 7.8% (n = 903). The Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) score provided the best discrimination (AUROC: 0.709 (95% CI: 0.692-0.727), yet no risk prediction model demonstrated good discrimination (AUROC >0.7). Conclusions The risk of PPC for patients following major abdominal surgery in the pre-covid era is not well described by existing prediction tools. New prediction tools are required to account for additional variation introduced for patients affected by SARS-CoV-2 infection.

scholarly journals V2 Evaluation of risk prediction models for postoperative pulmonary complications in adults undergoing major abdominal surgery: A systematic review and external validation study of the REspiratory COmplications after abdomiNal surgery (RECON) cohort

BJS Open ◽  
2021 ◽  
Vol 5 (Supplement_1) ◽  
Author(s):  
◽  
Omar Kouli
Keyword(s):  
Systematic Review ◽  
Abdominal Surgery ◽  
Risk Prediction ◽  
Prediction Models ◽  
External Validation ◽  
Pulmonary Complications ◽  
Major Abdominal Surgery ◽  
Postoperative Pulmonary Complications ◽  
Risk Prediction Models ◽  
Prediction Tools

Abstract Background Postoperative pulmonary complications (PPCs) following major abdominal surgery result in substantial morbidity and mortality, yet stratifying patients for risk-modifying interventions remains challenging. This study aimed to identify and externally validate PPC risk prediction models in an international, prospective cohort. Methods A systematic review was conducted to identify risk prediction models for PPC following abdominal surgery. External validation was performed using data from a prospective dataset of adult patients undergoing major abdominal surgery from January to April 2019 in the UK, Ireland and Australia. The primary outcome was identification of PPC within 30-days (StEP-COMPAC criteria definition). Model discrimination and diagnostic accuracy were compared. Results Six unique risk prediction models were eligible from 2819 records. These were validated across 11,591 patients, with an overall PPC rate of 7.8% (n = 903). The Assess Respiratory Risk in Surgical Patients in Catalonia (ARISCAT) score provided the best discrimination (AUC: 0.709 (95% CI: 0.692-0.727), yet no risk prediction model demonstrated good discrimination (AUC >0.7). Conclusion The risk of PPC for patients following major abdominal surgery in the pre-covid era is not well described by existing prediction tools. New prediction tools are required to account for additional variation introduced for patients affected by SARS-CoV-2 infection.

scholarly journals Studies of parenchymal texture added to mammographic breast density and risk of breast cancer: a systematic review of methods used in the literature.

2021 ◽  
Author(s):  
Akila Anandarajah ◽  
Yongzhen Chen ◽  
Graham A Colditz ◽  
Angela Hardi ◽  
Carolyn R Stoll ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Systematic Review ◽  
Prediction Models ◽  
Meta Analysis ◽  
External Validation ◽  
Texture Features ◽  
Mammographic Breast Density ◽  
Cochrane Library ◽  
Breast Cancers ◽  
Full Field

This systematic review aimed to assess the methods used to classify mammographic breast parenchymal features in relation to prediction of future breast cancer including the time from mammogram to diagnosis of breast cancer, and methods for the identification of texture features and selection of features for inclusion in analysis. The databases including Medline (Ovid) 1946-, Embase.com 1947-, CINAHL Plus 1937-, Scopus 1823-, Cochrane Library (including CENTRAL), and Clinicaltrials.gov. were searched through October 2021 to extract published articles in English describing the relationship of parenchymal texture features with risk of breast cancer. Twenty-eight articles published since 2016 were included in the final review. Of these, 7 assessed texture features from film mammograms images, 3 did not report details of the image used, and the others used full field mammograms from Hologic, GE and other manufacturers. The identification of parenchymal texture features varied from using a predefined list to machine-driven identification. Reduction in number of features chosen for analysis in relation to cancer incidence then varied across statistical approaches and machine learning methods. The variation in approach and number of features identified for inclusion in analysis precluded generating a quantitative summary or meta-analysis of the value of these to improve predicting risk of future breast cancers. This updated overview of the state of the art revealed research gaps; based on these, we provide recommendations for future studies using parenchymal features for mammogram images to make use of accumulating image data, and external validation of prediction models that extend to 5 and 10 years to guide clinical risk management. By following these recommendations, we expect to improve risk classification and risk prediction for women to tailor screening and prevention strategies to level of risk.

scholarly journals Models for predicting venous thromboembolism in ambulatory patients with lung cancer: a systematic review protocol

BMJ Open ◽  
2021 ◽  
Vol 11 (12) ◽  
pp. e055322
Author(s):  
Ann-Rong Yan ◽  
Indira Samarawickrema ◽  
Mark Naunton ◽  
Gregory M Peterson ◽  
Desmond Yip ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Systematic Review ◽  
Risk Assessment ◽  
Venous Thromboembolism ◽  
Risk Prediction ◽  
Prediction Models ◽  
Data Extraction ◽  
Cochrane Library ◽  
Risk Prediction Models ◽  
Ambulatory Patients

IntroductionVenous thromboembolism (VTE) is a common complication in patients with cancer and has a determining role in the disease prognosis. The risk is significantly increased with certain types of cancer, such as lung cancer. Partly due to difficulties in managing haemorrhage in outpatient settings, anticoagulant prophylaxis is only recommended for ambulatory patients at high risk of VTE. This requires a precise VTE risk assessment in individual patients. Although VTE risk assessment models have been developed and updated in recent years, there are conflicting reports on the effectiveness of such risk prediction models in patient management. The aim of this systematic review is to gain a better understanding of the available VTE risk assessment tools for ambulatory patients with lung cancer and compare their predictive performance.Methods and analysisA systematic review will be conducted using MEDLINE, Cochrane Library, CINAHL, Scopus and Web of Science databases from inception to 30 September 2021, to identify all reports published in English describing VTE risk prediction models which have included adult ambulatory patients with primary lung cancer for model development and/or validation. Two independent reviewers will conduct article screening, study selection, data extraction and quality assessment of the primary studies. Any disagreements will be referred to a third researcher to resolve. The included studies will be assessed for risk of bias and applicability. The Checklist for Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies will be used for data extraction and appraisal. Data from similar studies will be used for meta-analysis to determine the incidence of VTE and the performance of the risk models.Ethics and disseminationEthics approval is not required. We will disseminate the results in a peer-reviewed journal.PROSPERO registration numberCRD42021245907.

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