Evidence map›Paper›PMID 28710713›Full record

ArticleInternal and emergency medicine2017

A consensus for the development of a vector model to assess clinical complexity.

Gino Roberto Corazza, Catherine Klersy, Pietro Formagnana, Marco Vincenzo Lenti, Donatella Padula, Consensus Panel

Registry-linked trialAbstract readConsensus Statement
PubMed Publisher
In one paragraph

Article in Internal and emergency medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT03439410 (Development of a Mathematical Model of Clinical Complexity and of an Index for Its Practical Evaluation in Observational Prospective Longitudinal Studies. San MAtteo Complexity Study), which is not on this map. Cited by 14 papers.

0numbers the graph read from it
0cells of the map it votes in
14citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

NCT03439410 unknown statusnot on this map

Development of a Mathematical Model of Clinical Complexity and of an Index for Its Practical Evaluation in Observational Prospective Longitudinal Studies. San MAtteo Complexity Study

Typeobservational_patient_registrySponsorFondazione IRCCS Policlinico San Matteo di PaviaRan2017 to 2022Enrolled2,000ConditionsMultimorbidity, Health Knowledge, Attitudes, PracticeArmsClinical Complexity Index
3 · Its place in the literature

Who cites it

14 citing papers in PubMed.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

6 authors.

Gino Roberto CorazzaFirst Department of Internal Medicine, San Matteo Hospital Foundation, University of Pavia, Pavia, Italy. gr.corazza@smatteo.pv.it.
Catherine KlersyBiometry and Clinical Epidemiology, San Matteo Hospital Foundation, Pavia, Italy.
Pietro FormagnanaFirst Department of Internal Medicine, San Matteo Hospital Foundation, University of Pavia, Pavia, Italy.
Marco Vincenzo LentiFirst Department of Internal Medicine, San Matteo Hospital Foundation, University of Pavia, Pavia, Italy.
Donatella PadulaFirst Department of Internal Medicine, San Matteo Hospital Foundation, University of Pavia, Pavia, Italy.
Consensus Panel

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

The progressive rise in multimorbidity has made management of complex patients one of the most topical and challenging issues in medicine, both in clinical practice and for healthcare organizations. To make this easier, a score of clinical complexity (CC) would be useful. A vector model to evaluate biological and extra-biological (socio-economic, cultural, behavioural, environmental) domains of CC was proposed a few years ago. However, given that the variables that grade each domain had never been defined, this model has never been used in clinical practice. To overcome these limits, a consensus meeting was organised to grade each domain of CC, and to establish the hierarchy of the domains. A one-day consensus meeting consisting of a multi-professional panel of 25 people was held at our Hospital. In a preliminary phase, the proponents selected seven variables as qualifiers for each of the five above-mentioned domains. In the course of the meeting, the panel voted for five variables considered to be the most representative for each domain. Consensus was established with 2/3 agreement, and all variables were dichotomised. Finally, the various domains were parametrized and ranked within a feasible vector model. A Clinical Complexity Index was set up using the chosen variables. All the domains were graphically represented through a vector model: the biological domain was chosen as the most significant (highest slope), followed by the behavioural and socio-economic domains (intermediate slope), and lastly by the cultural and environmental ones (lowest slope). A feasible and comprehensive tool to evaluate CC in clinical practice is proposed herein.

Indexed as

Decision Support TechniquesPatient AcuityHumansMultimorbidityRisk AssessmentComorbidityInternal medicineMedical decision makingPatient complexityPolytherapyQuality of healthcare

Identifiers

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Registered trials

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.