ArticleResearch square2023
Predictive Models and Features of Patient Mortality across Dementia Types.
Jimmy Zhang, Luo Song, Kwun Chan, Zachary Miller, Kuan-Lin Huang
Open access · greenAbstract readPreprint
In one paragraphArticle in Research square, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
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 registryThe 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 literatureWho cites it
0 citing papers in PubMed, 0 citations in OpenAlex.
No citing paper in PubMed yet.
4 · The recordCorrections and comments
5 · Who and what moneyAuthors and funding
5 authors at 3 institutions in 2 countries.
Luo SongSchool of Medicine, The University of Queensland.
Kwun ChanNational Alzheimer's Coordinating Center, University of Washington.
Zachary MillerNational Alzheimer's Coordinating Center, University of Washington.
Icahn School of Medicine at Mount Sinai · USThe University of Queensland · AUUniversity of Washington · US
Funding
THERAPEUTIC EFFECTS OF INTRA-NASAL INSULIN DETEMIRP50AG005136 · NIA · UNIVERSITY OF WASHINGTON · PI GRABOWSKI, THOMAS J. · 1985 to 2019
$57.2MVIRUS-INDUCED BRAIN PATHOLOGYP50AG005131 · NIA · UNIVERSITY OF CALIFORNIA, SAN DIEGO · PI BREWER, JAMES B · 1985 to 2018
$55.4MWASHINGTON UNIVERSITY ALZHEIMERS DISEASE RESEARCH CENTERP50AG005681 · NIA · WASHINGTON UNIVERSITY · PI FAGAN, ANNE · 1985 to 2019
$52.1MSUPPLEMENT TO RUSH ALZHEIMERS DISEASE CENTER COREP30AG010161 · NIA · RUSH UNIVERSITY MEDICAL CENTER · PI LEURGANS, SUE E. · 1991 to 2020
$49.1MSatellite Diagnostic and Treatment Clinic CoreP50AG008702 · NIA · COLUMBIA UNIV NEW YORK MORNINGSIDE · PI DE JAGER, PHILIP L · 1989 to 2019
$46.3MNational Alzheimer's Coordinating CenterU24AG072122 · NIA · UNIVERSITY OF WASHINGTON · PI STEPHENS, KARI A · 2021 to 2025
$45.8MTREATMENT OF DEPRESSION IN ALZHEIMER'S DISEASEP50AG005133 · NIA · UNIVERSITY OF PITTSBURGH AT PITTSBURGH · PI KAMBOH, M. ILYAS · 1985 to 2019
$43.0MTHE ROLE OF ADVANCED CAA IN ALZHEIMER'S DEMENTIAP50AG005134 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI HYMAN, BRADLEY T. · 1985 to 2018
$42.1MVASCULAR RISK AND COGNITIVE STATUS IN A LATINO POPULATIONP50AG005142 · NIA · UNIVERSITY OF SOUTHERN CALIFORNIA · PI BRASKIE, MEREDITH NICOLE · 1985 to 2019
$41.0MVESICULAR LOCALIZATION AND FUNCTION OF PRESENILIN 1 FRAGMENTP50AG005138 · NIA · MOUNT SINAI SCHOOL OF MEDICINE OF NYU · PI GROSSMAN, HILLEL · 1985 to 2019
$37.9M'T V ASSESSMENT - MEMORY DISORDERED PATIENTS'P50AG005146 · NIA · JOHNS HOPKINS UNIVERSITY · PI ALBERT, MARILYN S. · 1985 to 2019
$37.6MResearch Education ComponentP30AG010133 · NIA · INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS · PI SAYKIN, ANDREW J · 1991 to 2020
$37.3MNIA NIH HHS P20 AG068024NIA NIH HHS P20 AG068053NIA NIH HHS P20 AG068077NIA NIH HHS P20 AG068082NIA NIH HHS P30 AG008017NIA NIH HHS P30 AG008051NIA NIH HHS P30 AG010124NIA NIH HHS P30 AG010129NIA NIH HHS P30 AG010133NIA NIH HHS P30 AG010161NIA NIH HHS P30 AG012300NIA NIH HHS P30 AG013846NIA NIH HHS P30 AG013854NIA NIH HHS P30 AG019610NIA NIH HHS P30 AG028383NIA NIH HHS P30 AG035982NIA NIH HHS P30 AG049638NIA NIH HHS P30 AG053760NIA NIH HHS P30 AG066546NIA NIH HHS P30 AG072958NIA NIH HHS P30 AG072959NIA NIH HHS P50 AG005131NIA NIH HHS P50 AG005133NIA NIH HHS P50 AG005134NIA NIH HHS P50 AG005136NIA NIH HHS P50 AG005138NIA NIH HHS P50 AG005142NIA NIH HHS P50 AG005146NIA NIH HHS P50 AG005681NIA NIH HHS P50 AG008702NIA NIH HHS P50 AG016573NIA NIH HHS P50 AG016574NIA NIH HHS P50 AG023501NIA NIH HHS P50 AG025688NIA NIH HHS P50 AG033514NIA NIH HHS P50 AG047266NIA NIH HHS P50 AG047270NIA NIH HHS P50 AG047366NIA NIH HHS U24 AG072122NIGMS NIH HHS R35 GM138113
6 · The paper itselfAbstract
Dementia care is challenging due to the divergent trajectories in disease progression and outcomes. Predictive models are needed to identify patients at risk of near-term mortality. Here, we developed machine learning models predicting survival using a dataset of 45,275 unique participants and 163,782 visit records from the U.S. National Alzheimer's Coordinating Center (NACC). Our models achieved an AUC-ROC of over 0.82 utilizing nine parsimonious features for all one-, three-, five-, and ten-year thresholds. The trained models mainly consisted of dementia-related predictors such as specific neuropsychological tests and were minimally affected by other age-related causes of death, e.g., stroke and cardiovascular conditions. Notably, stratified analyses revealed shared and distinct predictors of mortality across eight dementia types. Unsupervised clustering of mortality predictors grouped vascular dementia with depression and Lewy body dementia with frontotemporal lobar dementia. This study demonstrates the feasibility of flagging dementia patients at risk of mortality for personalized clinical management.
Identifiers
PMID36711767
PMCPMC9882612
OpenAlexW4317513525
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