Evidence map›Paper›PMID 42490354›Full record

ArticleRespiration; international review of thoracic diseases2026

Predictor of Mortality in Obstructive Sleep Apnea: Results of Explainable Deep Learning-Based Survival Analysis from a 15-Year Follow-Up.

Gaetano Pagano, Giorgio Castellana, Giovanni D Apos Addio, Lucia Palazzo, Maria Aliani, Patrizia Guido, Francesco Berloco, Pietro Maria Marvulli, Vladimiro Suglia, Simona Colucci and 3 more

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Article in Respiration; international review of thoracic diseases, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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0citing papers in PubMed
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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

0 citing papers in PubMed.

No citing paper in PubMed yet.

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

13 authors.

Gaetano PaganoIstituti Clinici Scientifici Maugeri, Bioengineering Unit of Bari Institute, IRCCS, Bari, Italy.
Giorgio CastellanaDivision of Pulmonary Disease and Rehabilitation, Istituti Clinici Scientifici Maugeri, IRCCS, Bari, Italy, giorgio.castellana@icsmaugeri.it.
Giovanni D Apos AddioIstituti Clinici Scientifici Maugeri IRCCS, Bioengineering Unit of Telese Terme Institute, Telese Terme, Italy.
Lucia PalazzoIstituti Clinici Scientifici Maugeri IRCCS, Bioengineering Unit of Bari Institute, IRCCS, Bari, Italy.
Maria AlianiDivision of Pulmonary Disease and Rehabilitation, Istituti Clinici Scientifici Maugeri, IRCCS, Bari, Italy.
Patrizia GuidoDivision of Pulmonary Disease and Rehabilitation, Istituti Clinici Scientifici Maugeri, IRCCS, Bari, Italy.
Francesco BerlocoDepartment of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy.
Pietro Maria MarvulliDepartment of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy.
Vladimiro SugliaDepartment of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy.
Simona ColucciDepartment of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy.
Donato LacedoniaDepartment of Specialistic Medicine, Respiratory Diseases Institute, University-Hospital Polyclinic of Foggia, Foggia, Italy.
Vitoantonio BevilacquaDepartment of Electrical and Information Engineering, Polytechnic of Bari, Bari, Italy.
Mauro CaroneDivision of Pulmonary Disease and Rehabilitation, Istituti Clinici Scientifici Maugeri, IRCCS, Bari, Italy.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

introductionObstructive sleep apnea (OSA) is a respiratory disorder with comorbidities of several nature, from cardiovascular to renal ones. OSA is typically treated through continuous positive airway pressure (CPAP), but more investigations are needed to confirm its benefits.

methodsIn this work, survival analysis has been exploited and enhanced with eXplainable Artifical Intelligence (XAI) to investigate the impact of comorbidities and compare the model reliability. The dataset encompasses both clinical and polysomnography-based data for a total of 45 different features.

resultsA total of 1,394 OSA patients followed for 15 years were enrolled. All the selected features have been studied by means of DL-models and time-dependent XAI techniques. The variables impacting on mortality are reported below in descending order with respect their importance: (1) age, years of CPAP, renal dysfunction, COPD, body mass index categories, sex, and anemia for the CoxTime; (2) age, apnea hypopnea index, renal dysfunction, SaO

conclusionAdvancing age, severity of OSA, comorbidity, including chronic kidney disease, COPD, and anemia, are crucial contributors to increased mortality.

Indexed as

Deep learningExplainable artificial intelligenceObstructive sleep apneaRehabilitation medicineSurvival analysis

Identifiers

PMID42490354
PMCPMC13557703

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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.