Soft clustering using real-world data for the identification of multimorbidity patterns in an elderly population: Cross-sectional study in a Mediterranean population

The aim of this study was to identify, with soft clustering methods, multimorbidity patterns in the electronic health records of a population =65 years, and to analyse such patterns in accordance with the different prevalence cut-off points applied. Fuzzy cluster analysis allows individuals to be linked simultaneously to multiple clusters and is more consistent with clinical experience than other approaches frequently found in the literature.Peer ReviewedPostprint (published version

Development and internal validation of prognostic models to predict negative health outcomes in older patients with multimorbidity and polypharmacy in general practice

Background Polypharmacy interventions are resource-intensive and should be targeted to those at risk of negative health outcomes. Our aim was to develop and internally validate prognostic models to predict health-related quality of life (HRQoL) and the combined outcome of falls, hospitalisation, institutionalisation and nursing care needs, in older patients with multimorbidity and polypharmacy in general practices. Methods Design: two independent data sets, one comprising health insurance claims data (n=592 456), the other data from the PRIoritising MUltimedication in Multimorbidity (PRIMUM) cluster randomised controlled trial (n=502). Population: >= 60 years, >= 5 drugs, >= 3 chronic diseases, excluding dementia. Outcomes: combined outcome of falls, hospitalisation, institutionalisation and nursing care needs (after 6, 9 and 24 months) (claims data); and HRQoL (after 6 and 9 months) (trial data). Predictor variables in both data sets: age, sex, morbidity-related variables (disease count), medication-related variables (European Union-Potentially Inappropriate Medication list (EU-PIM list)) and health service utilisation. Predictor variables exclusively in trial data: additional socio-demographics, morbidity-related variables (Cumulative Illness Rating Scale, depression), Medication Appropriateness Index (MAI), lifestyle, functional status and HRQoL (EuroQol EQ-5D-3L). Analysis: mixed regression models, combined with stepwise variable selection, 10-fold cross validation and sensitivity analyses. Results Most important predictors of EQ-5D-3L at 6 months in best model (Nagelkerke's R-2 0.507) were depressive symptoms (-2.73 (95% CI: -3.56 to -1.91)), MAI (-0.39 (95% CI: -0.7 to -0.08)), baseline EQ-5D-3L (0.55 (95% CI: 0.47 to 0.64)). Models based on claims data and those predicting long-term outcomes based on both data sets produced low R-2 values. In claims data-based model with highest explanatory power (R-2=0.16), previous falls/fall-related injuries, previous hospitalisations, age, number of involved physicians and disease count were most important predictor variables. Conclusions Best trial data-based model predicted HRQoL after 6 months well and included parameters of well-being not found in claims. Performance of claims data-based models and models predicting long-term outcomes was relatively weak. For generalisability, future studies should refit models by considering parameters representing well-being and functional status

Supervised machine learning algorithms can classify open-text feedback of doctor performance with human-level accuracy

Background: Machine learning techniques may be an effective and efficient way to classify open-text reports on doctor’s activity for the purposes of quality assurance, safety, and continuing professional development. Objective: The objective of the study was to evaluate the accuracy of machine learning algorithms trained to classify open-text reports of doctor performance and to assess the potential for classifications to identify significant differences in doctors’ professional performance in the United Kingdom. Methods: We used 1636 open-text comments (34,283 words) relating to the performance of 548 doctors collected from a survey of clinicians’ colleagues using the General Medical Council Colleague Questionnaire (GMC-CQ). We coded 77.75% (1272/1636) of the comments into 5 global themes (innovation, interpersonal skills, popularity, professionalism, and respect) using a qualitative framework. We trained 8 machine learning algorithms to classify comments and assessed their performance using several training samples. We evaluated doctor performance using the GMC-CQ and compared scores between doctors with different classifications using t tests. Results: Individual algorithm performance was high (range F score=.68 to .83). Interrater agreement between the algorithms and the human coder was highest for codes relating to “popular” (recall=.97), “innovator” (recall=.98), and “respected” (recall=.87) codes and was lower for the “interpersonal” (recall=.80) and “professional” (recall=.82) codes. A 10-fold cross-validation demonstrated similar performance in each analysis. When combined together into an ensemble of multiple algorithms, mean human-computer interrater agreement was .88. Comments that were classified as “respected,” “professional,” and “interpersonal” related to higher doctor scores on the GMC-CQ compared with comments that were not classified (P.05). Conclusions: Machine learning algorithms can classify open-text feedback of doctor performance into multiple themes derived by human raters with high performance. Colleague open-text comments that signal respect, professionalism, and being interpersonal may be key indicators of doctor’s performance

Differences in results and related factors between hospital-at-home modalities in Catalonia: a cross-sectional study

Average stay; Cross-sectional study; Hospital-at-home; MortalityEstancia media; Estudio transversal; Hospital en casa; MortalidadEstada mitjana; Estudi transversal; Hospital a casa; MortalitatHospital-at-home (HaH) is a healthcare modality that provides active treatment by healthcare staff in the patient's home for a condition that would otherwise require hospitalization. The aims were to describe the characteristics of different types of hospital-at-home (HaH), assess their results, and examine which factors could be related to these results. A cross-sectional study based on data from all 2014 HaH contacts from Catalonia was designed. The following HaH modalities were considered-admission avoidance (n = 7,214; 75.1%) and early assisted discharge (n = 2,387; 24.9%). The main outcome indicators were readmission, mortality, and length of stay (days). Multivariable models were fitted to assess the association between explanatory factors and outcomes. Hospital admission avoidance is a scheme in which, instead of being admitted to acute care hospitals, patients are directly treated in their own homes. Early assisted discharge is a scheme in which hospital in-care patients continue their treatment at home. In the hospital avoidance modality, there were 8.3% readmissions, 0.9% mortality, and a mean length of stay (SD) of 9.6 (10.6) days. In the early assisted discharge modality, these figures were 7.9%, 0.5%, and 9.8 (11.1), respectively. In both modalities, readmission and mean length of stay were related to comorbidity and type of hospital, and mortality with age. The results of HaH in Catalonia are similar to those observed in other contexts. The factors related to these results identified might help to improve the effectiveness and efficiency of the different HaH modalities

FUNERAL EN HONOR DE JORGE VI, REY DE INGLATERRA [Material gráfico]

REPORTAJE FOTOGRÁFICO DE JULIÁN HERNÁNDEZ GIL SOBRE EL FUNERAL EN HONOR DE JORGE VICopia digital. Madrid : Ministerio de Educación, Cultura y Deporte. Subdirección General de Coordinación Bibliotecaria, 201

How do aggregated patient reported outcome measures (PROMs) data stimulate health care improvement? A realist synthesis

Objectives: Internationally, there has been considerable debate about the role of data in supporting quality improvement in health care. Our objective was to understand how, why and in what circumstances the feedback of aggregated patient-reported outcome measures data improved patient care. Methods: We conducted a realist synthesis. We identified three main programme theories underlying the use of patient-reported outcome measures as a quality improvement strategy and expressed them as nine ‘if then’ propositions. We identified international evidence to test these propositions through searches of electronic databases and citation tracking, and supplemented our synthesis with evidence from similar forms of performance data. We synthesized this evidence through comparing the mechanisms and impact of patient-reported outcome measures and other performance data on quality improvement in different contexts. Results: Three programme theories were identified: supporting patient choice, improving accountability and enabling providers to compare their performance with others. Relevant contextual factors were extent of public disclosure, use of financial incentives, perceived credibility of the data and the practicality of the results. Available evidence suggests that patients or their agents rarely use any published performance data when selecting a provider. The perceived motivation behind public reporting is an important determinant of how providers respond. When clinicians perceived that performance indicators were not credible but were incentivized to collect them, gaming or manipulation of data occurred. Outcome data do not provide information on the cause of poor care: providers needed to integrate and interpret patient-reported outcome measures and other outcome data in the context of other data. Lack of timeliness of performance data constrains their impact. Conclusions: Although there is only limited research evidence to support some widely held theories of how aggregated patient-reported outcome measures data stimulate quality improvement, several lessons emerge from interventions sharing the same programme theories to help guide the increasing use of these measures

Use of computerised adaptive testing to reduce the number of items in patient-reported hip and knee outcome scores: an analysis of the NHS England National Patient-Reported Outcome Measures programme

10.1136/bmjopen-2021-059415BMJ OPEN12

Comparative analysis of methods for identifying multimorbidity patterns : a study of 'real-world' data

The aim was to compare multimorbidity patterns identified with the two most commonly used methods: hierarchical cluster analysis (HCA) and exploratory factor analysis (EFA) in a large primary care database. Specific objectives were: (1) to determine whether choice of method affects the composition of these patterns and (2) to consider the potential application of each method in the clinical setting. Cross-sectional study. Diagnoses were based on the 263 corresponding blocks of the International Classification of Diseases version 10. Multimorbidity patterns were identified using HCA and EFA. Analysis was stratified by sex, and results compared for each method. Electronic health records for 408 994 patients with multimorbidity aged 45-64 years in 274 primary health care teams from 2010 in Catalonia, Spain. HCA identified 53 clusters for women, with just 12 clusters including at least 2 diagnoses, and 15 clusters for men, all of them including at least two diagnoses. EFA showed 9 factors for women and 10 factors for men. We observed differences by sex and method of analysis, although some patterns were consistent. Three combinations of diseases were observed consistently across sex groups and across both methods: hypertension and obesity, spondylopathies and deforming dorsopathies, and dermatitis eczema and mycosis. This study showed that multimorbidity patterns vary depending on the method of analysis used (HCA vs EFA) and provided new evidence about the known limitations of attempts to compare multimorbidity patterns in real-world data studies. We found that EFA was useful in describing comorbidity relationships and HCA could be useful for in-depth study of multimorbidity. Our results suggest possible applications for each of these methods in clinical and research settings, and add information about some aspects that must be considered in standardisation of future studies: spectrum of diseases, data usage and methods of analysis

The International Classification of Functioning, Disability and Health: development of capacity and performance scales

Objective: There has been no attempt to obtain a continuous summary measure of disability from the checklist of the International Classification of Functioning, Disability and Health (ICF). Our objective was to assess whether valid scales of Capacity and Performance could be developed from the "Activities and Participation" domain of the ICF checklist.Study design and setting: A multicenter, observational study of 1,092 patients with 12 different chronic conditions from five European countries was conducted. Exploratory and confirmatory factor analyses were performed to assess the underlying factor structure. Reliability and validity of the Capacity and Performance scales were evaluated. Construct validity was assessed calculating effect size coefficients between extreme severity groups (discriminant ability).Results: The good fit of the confirmatory factor model supported the global scales of Capacity and Performance and their "Psychosocial" and "Physical" subscales. Reliability was excellent (coefficients=0.79-0.92). Effect sizes of most conditions were large for the Capacity global scale (0.50-3.05), and slightly lower for the Performance global scale (0.45-2.57).Conclusions: Our findings support the measurement model, reliability, and validity of the Capacity and Performance scales. Summary measures of functioning based on the ICF can be obtained using these scales, which should facilitate their incorporation in clinical and epidemiological studies