scholarly journals External validation of EPIC’s Risk of Unplanned Readmission model, the LACE+ index and SQLape as predictors of unplanned hospital readmissions: A monocentric, retrospective, diagnostic cohort study in Switzerland

PLoS ONE ◽  
2021 ◽  
Vol 16 (11) ◽  
pp. e0258338
Author(s):  
Aljoscha Benjamin Hwang ◽  
Guido Schuepfer ◽  
Mario Pietrini ◽  
Stefan Boes
Keyword(s):  
Cohort Study ◽  
Quality Of Care ◽  
Prediction Models ◽  
Hospital Readmissions ◽  
External Validation ◽  
Superior Performance ◽  
Study Endpoint ◽  
Unplanned Readmission ◽  
S Model

Introduction Readmissions after an acute care hospitalization are relatively common, costly to the health care system, and are associated with significant burden for patients. As one way to reduce costs and simultaneously improve quality of care, hospital readmissions receive increasing interest from policy makers. It is only relatively recently that strategies were developed with the specific aim of reducing unplanned readmissions using prediction models to identify patients at risk. EPIC’s Risk of Unplanned Readmission model promises superior performance. However, it has only been validated for the US setting. Therefore, the main objective of this study is to externally validate the EPIC’s Risk of Unplanned Readmission model and to compare it to the internationally, widely used LACE+ index, and the SQLAPE® tool, a Swiss national quality of care indicator. Methods A monocentric, retrospective, diagnostic cohort study was conducted. The study included inpatients, who were discharged between the 1st of January 2018 and the 31st of December 2019 from the Lucerne Cantonal Hospital, a tertiary-care provider in Central Switzerland. The study endpoint was an unplanned 30-day readmission. Models were replicated using the original intercept and beta coefficients as reported. Otherwise, score generator provided by the developers were used. For external validation, discrimination of the scores under investigation were assessed by calculating the area under the receiver operating characteristics curves (AUC). Calibration was assessed with the Hosmer-Lemeshow X2 goodness-of-fit test This report adheres to the TRIPOD statement for reporting of prediction models. Results At least 23,116 records were included. For discrimination, the EPIC´s prediction model, the LACE+ index and the SQLape® had AUCs of 0.692 (95% CI 0.676–0.708), 0.703 (95% CI 0.687–0.719) and 0.705 (95% CI 0.690–0.720). The Hosmer-Lemeshow X2 tests had values of p<0.001. Conclusion In summary, the EPIC´s model showed less favorable performance than its comparators. It may be assumed with caution that the EPIC´s model complexity has hampered its wide generalizability—model updating is warranted.

scholarly journals Quality of care and 30 day mortality among patients with hip fractures: a nationwide cohort study

2009 ◽  
Vol 9 (1) ◽  
Author(s):  
Katrine A Nielsen ◽  
Niels C Jensen ◽  
Claus M Jensen ◽  
Marianne Thomsen ◽  
Lars Pedersen ◽  
...  
Keyword(s):  
Cohort Study ◽  
Quality Of Care ◽  
Hip Fractures

Abstract 196: Development and Validation of Machine Learning-based Race-specific Models to Predict 10-year Risk of Heart Failure: A Multi-cohort Analysis

Circulation ◽  
2020 ◽  
Vol 142 (Suppl_3) ◽  
Author(s):  
Matthew W Segar ◽  
Byron Jaeger ◽  
Kershaw V Patel ◽  
Vijay Nambi ◽  
Chiadi E Ndumele ◽  
...  
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Heart Failure ◽  
Risk Prediction ◽  
Prediction Models ◽  
External Validation ◽  
Superior Performance ◽  
Risk Models ◽  
Black And White ◽  
White Adults

Introduction: Heart failure (HF) risk and the underlying biological risk factors vary by race. Machine learning (ML) may improve race-specific HF risk prediction but this has not been fully evaluated. Methods: The study included participants from 4 cohorts (ARIC, DHS, JHS, and MESA) aged > 40 years, free of baseline HF, and with adjudicated HF event follow-up. Black adults from JHS and white adults from ARIC were used to derive race-specific ML models to predict 10-year HF risk. The ML models were externally validated in subgroups of black and white adults from ARIC (excluding JHS participants) and pooled MESA/DHS cohorts and compared to prior established HF risk scores developed in ARIC and MESA. Harrell’s C-index and Greenwood-Nam-D’Agostino chi-square were used to assess discrimination and calibration, respectively. Results: In the derivation cohorts, 288 of 4141 (7.0%) black and 391 of 8242 (4.7%) white adults developed HF over 10 years. The ML models had excellent discrimination in both black and white participants (C-indices = 0.88 and 0.89). In the external validation cohorts for black participants from ARIC (excluding JHS, N = 1072) and MESA/DHS pooled cohorts (N = 2821), 131 (12.2%) and 115 (4.1%) developed HF. The ML model had adequate calibration and demonstrated superior discrimination compared to established HF risk models (Fig A). A consistent pattern was also observed in the external validation cohorts of white participants from the MESA/DHS pooled cohorts (N=3236; 100 [3.1%] HF events) (Fig A). The most important predictors of HF in both races were NP levels. Cardiac biomarkers and glycemic parameters were most important among blacks while LV hypertrophy and prevalent CVD and traditional CV risk factors were the strongest predictors among whites (Fig B). Conclusions: Race-specific and ML-based HF risk models that integrate clinical, laboratory, and biomarker data demonstrated superior performance when compared to traditional risk prediction models.

scholarly journals A systematic review of the quality of clinical prediction models in in vitro fertilisation

2020 ◽  
Vol 35 (1) ◽  
pp. 100-116 ◽  
Author(s):  
M B Ratna ◽  
S Bhattacharya ◽  
B Abdulrahim ◽  
D J McLernon
Keyword(s):  
Systematic Review ◽  
Live Birth ◽  
Prediction Models ◽  
Conflicts Of Interest ◽  
External Validation ◽  
Post Treatment ◽  
Treatment Model ◽  
Clinical Prediction ◽  
Clinical Prediction Models

Abstract STUDY QUESTION What are the best-quality clinical prediction models in IVF (including ICSI) treatment to inform clinicians and their patients of their chance of success? SUMMARY ANSWER The review recommends the McLernon post-treatment model for predicting the cumulative chance of live birth over and up to six complete cycles of IVF. WHAT IS KNOWN ALREADY Prediction models in IVF have not found widespread use in routine clinical practice. This could be due to their limited predictive accuracy and clinical utility. A previous systematic review of IVF prediction models, published a decade ago and which has never been updated, did not assess the methodological quality of existing models nor provided recommendations for the best-quality models for use in clinical practice. STUDY DESIGN, SIZE, DURATION The electronic databases OVID MEDLINE, OVID EMBASE and Cochrane library were searched systematically for primary articles published from 1978 to January 2019 using search terms on the development and/or validation (internal and external) of models in predicting pregnancy or live birth. No language or any other restrictions were applied. PARTICIPANTS/MATERIALS, SETTING, METHODS The PRISMA flowchart was used for the inclusion of studies after screening. All studies reporting on the development and/or validation of IVF prediction models were included. Articles reporting on women who had any treatment elements involving donor eggs or sperm and surrogacy were excluded. The CHARMS checklist was used to extract and critically appraise the methodological quality of the included articles. We evaluated models’ performance by assessing their c-statistics and plots of calibration in studies and assessed correct reporting by calculating the percentage of the TRIPOD 22 checklist items met in each study. MAIN RESULTS AND THE ROLE OF CHANCE We identified 33 publications reporting on 35 prediction models. Seventeen articles had been published since the last systematic review. The quality of models has improved over time with regard to clinical relevance, methodological rigour and utility. The percentage of TRIPOD score for all included studies ranged from 29 to 95%, and the c-statistics of all externally validated studies ranged between 0.55 and 0.77. Most of the models predicted the chance of pregnancy/live birth for a single fresh cycle. Six models aimed to predict the chance of pregnancy/live birth per individual treatment cycle, and three predicted more clinically relevant outcomes such as cumulative pregnancy/live birth. The McLernon (pre- and post-treatment) models predict the cumulative chance of live birth over multiple complete cycles of IVF per woman where a complete cycle includes all fresh and frozen embryo transfers from the same episode of ovarian stimulation. McLernon models were developed using national UK data and had the highest TRIPOD score, and the post-treatment model performed best on external validation. LIMITATIONS, REASONS FOR CAUTION To assess the reporting quality of all included studies, we used the TRIPOD checklist, but many of the earlier IVF prediction models were developed and validated before the formal TRIPOD reporting was published in 2015. It should also be noted that two of the authors of this systematic review are authors of the McLernon model article. However, we feel we have conducted our review and made our recommendations using a fair and transparent systematic approach. WIDER IMPLICATIONS OF THE FINDINGS This study provides a comprehensive picture of the evolving quality of IVF prediction models. Clinicians should use the most appropriate model to suit their patients’ needs. We recommend the McLernon post-treatment model as a counselling tool to inform couples of their predicted chance of success over and up to six complete cycles. However, it requires further external validation to assess applicability in countries with different IVF practices and policies. STUDY FUNDING/COMPETING INTEREST(S) The study was funded by the Elphinstone Scholarship Scheme and the Assisted Reproduction Unit, University of Aberdeen. Both D.J.M. and S.B. are authors of the McLernon model article and S.B. is Editor in Chief of Human Reproduction Open. They have completed and submitted the ICMJE forms for Disclosure of potential Conflicts of Interest. The other co-authors have no conflicts of interest to declare. REGISTRATION NUMBER N/A

scholarly journals Can the adherence to quality of care indicators for early rheumatoid arthritis in clinical practice reduce risk of hospitalisation? Retrospective cohort study based on the Record Linkage of Rheumatic Disease study of the Italian Society for Rheumatology

BMJ Open ◽  
2020 ◽  
Vol 10 (9) ◽  
pp. e038295
Author(s):  
Anna Zanetti ◽  
Carlo Alberto Scirè ◽  
Lisa Argnani ◽  
Greta Carrara ◽  
Antonella Zambon
Keyword(s):  
Rheumatoid Arthritis ◽  
Cohort Study ◽  
Quality Of Care ◽  
Retrospective Cohort Study ◽  
Lower Risk ◽  
Retrospective Cohort ◽  
Quality Standards ◽  
Standards Of Care ◽  
Adherence Score

ObjectiveTo describe the adherence to quality of care indicators in early rheumatoid arthritis (RA) and to evaluate its impact on the risk of hospitalisation in a real-world setting.DesignRetrospective cohort study.SettingPatients with early-onset RA identified from healthcare regional administrative databases by means of a validated algorithm between 2006 and 2012 in the Lombardy region (Italy).ParticipantsThe study cohort included 14 203 early-onset RA (71% female, mean age 60 years).Outcome measuresFor each patient, a summary adherence score was calculated starting from the compliance to six quality indicators: (1–2) methotrexate or sulfasalazine or leflunomide with/without glucocorticoids, (3–4) other disease-modifying antirheumatic drugs (DMARDs) with/without glucocorticoids, (5) early interruption of glucocorticoids, (6) early clinical assessment.The relationship between low, intermediate and high categories of the summary score and the 12-month risk of hospitalisation for all causes and for RA was assessed.ResultsDuring a follow-up of 1 year, 2609 hospitalisations occurred, of which 704 were for RA (main or secondary diagnosis) and 252 primarily for RA. In a 7-year period (2006–2012), early DMARDs and timely clinical monitoring treatment increased (from 52% to 62% p trend <0.001 and from 25% to 30% p trend 0.009, respectively).Intermediate and high summary adherence score categories (compared with the low category) were related significantly with a lower risk of hospitalisation (adjusted HR 0.85 (95% CI 0.77 to 0.93), p<0.001 and HR 0.76 (95% CI 0.69 to 0.84), p<0.001, respectively). Among the indicators of the adherence score, early DMARD prescription showed the strongest positive impact, while long-term use of glucocorticoids was the worst negative one.ConclusionIn early RA, adherence to quality standards of care is associated with a lower risk of hospitalisation. Future interventions to improve the adherence to quality standards of care in this setting should decrease the risk of hospitalisation with a significant impact on individual and population health.

scholarly journals Quality of care in inflammatory bowel disease: results of a prospective controlled cohort study in Germany (NETIBD)

2017 ◽  
Vol Volume 10 ◽  
pp. 215-227 ◽  
Author(s):  
Jana Langbrandtner ◽  
Angelika Hüppe ◽  
Petra Jessen ◽  
Jürgen Büning ◽  
Susanna Nikolaus ◽  
...  
Keyword(s):  
Inflammatory Bowel Disease ◽  
Cohort Study ◽  
Quality Of Care ◽  
Bowel Disease ◽  
Inflammatory Bowel

scholarly journals Self-reported quality of care for older adults from 2004 to 2011: a cohort study

2014 ◽  
Vol 43 (5) ◽  
pp. 716-720 ◽  
Author(s):  
N. Steel ◽  
A. C. Hardcastle ◽  
A. Clark ◽  
L. T. A. Mounce ◽  
M. O. Bachmann ◽  
...  
Keyword(s):  
Older Adults ◽  
Cohort Study ◽  
Quality Of Care

scholarly journals Characteristics of Syrian and Lebanese Diabetes and Hypertension Patients in Lebanon

2018 ◽  
Vol 10 (1) ◽  
pp. 60-75 ◽  
Author(s):  
Shannon Doocy ◽  
Emily Lyles ◽  
Zeina Fahed ◽  
Abdalla Mkanna ◽  
Kaisa Kontunen ◽  
...  
Keyword(s):  
Cohort Study ◽  
Quality Of Care ◽  
Health Status ◽  
Treatment Guidelines ◽  
Communicable Disease ◽  
Low Cost ◽  
Health Care Facilities ◽  
Longitudinal Cohort Study ◽  
Longitudinal Cohort

Background:Given the protracted nature of the crisis in Syria, the large caseload of Syrian refugee patients with non-communicable diseases, and the high costs of providing non-communicable disease care, implications for Lebanon’s health system are vast.Objective:To provide a profile of the health status of diabetes and hypertension patients enrolled in a longitudinal cohort study in Lebanon.Methods:A longitudinal cohort study was implemented from January 2015 through August 2016 to evaluate the effectiveness of treatment guidelines and an mHealth application on the quality of care and health outcomes for patients in primary health care facilities in Lebanon offering low-cost services serving both Syrian refugees and Lebanese host communities. This paper presents baseline characteristics of enrolled patients, providing an overall health status profile.Results:Among participants, 11.5% of patients with hypertension and 9.7% of patients with diabetes were previously undiagnosed. Quality of care, measured as the proportion of patients with biometrics reported and whose condition is controlled, is less than ideal and varied by geographic location. Controlled blood pressure measurements were observed in 64.2% of patients with hypertension; HbA1C values indicated diabetes control in 43.5% of the patients.Conclusion:Differences in diagnostic history and disease control between Syrian and Lebanese patients and across geographic regions were observed, which could be applied to inform strategies aimed at improving diagnosis and quality of care for hypertension and diabetes in primary care settings in Lebanon.

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