Evidence map›Paper›PMID 34610032›Full record

ArticlePloS one2021

Condensed trajectory of the temporal correlation of diseases and mortality extracted from over 300,000 patients in hospitals.

Hyojung Paik, Jimin Kim

Open access · goldAbstract readMulticenter Study
In one paragraph

Article in PloS one, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.

0numbers the graph read from it
0cells of the map it votes in
7citing papers in PubMed
1.1field-weighted citation impact, top 15% 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

7 citing papers in PubMed, 9 citations in OpenAlex.

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4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

2 authors at 1 institution in 1 country.

Hyojung PaikDivision of National Supercomputing, Center for Supercomputing Applications, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea.
Jimin KimDivision of National Supercomputing, Center for Supercomputing Applications, Korea Institute of Science and Technology Information, Daejeon, Republic of Korea.
Korea Institute of Science & Technology Information · KR

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Understanding mortality, derived from debilitations consisting of multiple diseases, is crucial for patient stratification. Here, in systematic fashion, we report comprehensive mortality data that map the temporal correlation of diseases that tend toward deaths in hospitals. We used a mortality trajectory model that represents the temporal ordering of disease appearance, with strong correlations, that terminated in fatal outcomes from one initial diagnosis in a set of patients throughout multiple admissions. Based on longitudinal healthcare records of 10.4 million patients from over 350 hospitals, we profiled 300 mortality trajectories, starting from 118 diseases, in 311,309 patients. Three-quarters (75%) of 59,794 end-stage patients and their deaths accrued throughout 160,360 multiple disease appearances in a short-term period (<4 years, 3.5 diseases per patient). This overlooked and substantial heterogeneity of disease patients and outcomes in the real world is unraveled in our trajectory map at the disease-wide level. For example, the converged dead-end in our trajectory map presents an extreme diversity of sepsis patients based on 43 prior diseases, including lymphoma and cardiac diseases. The trajectories involving the largest number of deaths for each age group highlight the essential predisposing diseases, such as acute myocardial infarction and liver cirrhosis, which lead to over 14,000 deaths. In conclusion, the deciphering of the debilitation processes of patients, consisting of the temporal correlations of diseases that tend towards hospital death at a population-wide level is feasible.

Indexed as

HospitalizationHospital MortalityHospitalsAdolescentAdultAgedCaliforniaCardiovascular DiseasesChildChild, PreschoolComorbidityFemaleHumansInfantInfant, NewbornLongitudinal Studies

Identifiers

PMID34610032
PMCPMC8491897
OpenAlexW3202750731

What OpenQuestion holds

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LicenceCC BY
Read underepoch 390

Registered trials

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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.