Evidence map›Paper›PMID 35849196›Full record

ArticleNeurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology2022

Development and internal validation of a prognostic model for 15-year risk of Alzheimer dementia in primary care patients.

Giulia Grande, Davide L Vetrano, Ettore Marconi, Elisa Bianchini, Iacopo Cricelli, Valeria Lovato, Luisa Guglielmini, Daiana Taddeo, Stefano F Cappa, Claudio Cricelli and 1 more

Abstract read
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In one paragraph

Article in Neurological sciences : official journal of the Italian Neurological Society and of the Italian Society of Clinical Neurophysiology, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.6field-weighted citation impact, top 17% of its field
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.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Article
  3. Article
  4. Article
  5. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

11 authors at 4 institutions in 2 countries.

Giulia GrandeAging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden.
Davide L VetranoAging Research Center, Department of Neurobiology, Care Sciences and Society, Karolinska Institutet and Stockholm University, Stockholm, Sweden.
Ettore MarconiHealth Search, Italian College of General Practitioners and Primary Care, Via del Sansovino 179, 50141, Florence, Italy.
Elisa BianchiniHealth Search, Italian College of General Practitioners and Primary Care, Via del Sansovino 179, 50141, Florence, Italy.
Iacopo CricelliHealth Search, Italian College of General Practitioners and Primary Care, Via del Sansovino 179, 50141, Florence, Italy.
Valeria LovatoRoche S.P.A, Monza, Italy.
Luisa GuglielminiRoche S.P.A, Monza, Italy.
Daiana TaddeoItalian College of General Practitioners and Primary Care, Florence, Italy.
Stefano F CappaICoN Center, University Institute for Advanced Studies IUSS Pavia, Pavia, Italy.
Claudio CricelliItalian College of General Practitioners and Primary Care, Florence, Italy.
Francesco LapiHealth Search, Italian College of General Practitioners and Primary Care, Via del Sansovino 179, 50141, Florence, Italy. lapi.francesco@simg.it.ORCID http://orcid.org/0000-0002-4342-9128
Società Italiana di Medicina Generale · ITRoche (Italy) · ITStockholm University · SEIstituto Universitario di Studi Superiori di Pavia · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe exploitation of routinely collected clinical health information is warranted to optimize the case detection and diagnostic workout of Alzheimer's disease (AD). We aimed to derive an AD prediction score based on routinely collected primary care data.

methodsWe built a cohort selecting 199,978 primary care patients 60 + part of the Health Search Database between January 2002 and 2009, followed up until 2019 to detect incident AD cases. The cohort was randomly divided into a derivation and validation sub-cohort. To identify AD and non-AD cases, we applied a clinical algorithm that involved two clinicians. According to a nested case-control design, AD cases were matched with up to 10 controls based on age, sex, calendar period, and follow-up duration. Using the derivation sub-cohort, 32 potential AD predictors (sociodemographic, clinical, drug-related, etc.) were tested in a logistic regression and selected to build a prediction model. The predictive performance of this model was tested on the validation sub-cohort by mean of explained variation, calibration, and discrimination measurements.

resultsWe identified 3223 AD cases. The presence of memory disorders, hallucinations, anxiety, and depression and the use of NSAIDs were associated with future AD. The combination of the predictors allowed the production of a predictive score that showed an explained variation (pseudo-R

conclusionAn automated risk score for AD based on routinely collected clinical data is a promising tool for the early case detection and timely management of patients by the general practitioners.

Indexed as

Alzheimer DiseaseCase-Control StudiesCohort StudiesFemaleHumansMalePrimary Health CarePrognosisAlzheimer’s diseaseDementiaPredictionPrimary care

Identifiers

PMID35849196
OpenAlexW4285727109

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.