Evidence map›Paper›PMID 39444849›Full record

ArticleJournal of thoracic disease2024

Predictive model for intraoperative decision-making in video-assisted thoracoscopic lobectomy: optimizing chest drain placement for high-risk patients.

Oleg Pikin, Andrey Ryabov, Oleg Aleksandrov, Evgeniy Toneev, Denis Larionov, Airat Garifullin, Yuri Esakov

Abstract read
In one paragraph

Article in Journal of thoracic disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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.

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

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Observational
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

7 authors.

Oleg PikinDepartment of Thoracic Surgery, P. Hertsen Moscow Oncology Research Institute (MORI), Moscow, Russia.ORCID https://orcid.org/0000-0001-6871-6804
Andrey RyabovDepartment of Thoracic Surgery, P. Hertsen Moscow Oncology Research Institute (MORI), Moscow, Russia.ORCID https://orcid.org/0000-0002-1037-2364
Oleg AleksandrovDepartment of Thoracic Surgery, P. Hertsen Moscow Oncology Research Institute (MORI), Moscow, Russia.ORCID https://orcid.org/0000-0002-4131-9179
Evgeniy ToneevDepartment of Thoracic Surgery, Regional Clinical Oncological Dispensary, Ulyanovsk, Russia.
Denis LarionovDepartment of Thoracic Surgery, P. Hertsen Moscow Oncology Research Institute (MORI), Moscow, Russia.ORCID https://orcid.org/0000-0002-7437-2646
Airat GarifullinDepartment of Thoracic Surgery, P. Hertsen Moscow Oncology Research Institute (MORI), Moscow, Russia.ORCID https://orcid.org/0000-0002-7616-3899
Yuri EsakovThird Oncological Department, Clinical Oncological Hospital #1, Moscow, Russia.ORCID https://orcid.org/0000-0002-5933-924X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: For years, it has been the common and widely accepted practice in thoracic surgery to place apical and basal drains after a lobectomy to completely drain the pleural cavity. With the development of thoracoscopic technology, it became apparent that the use of a single chest tube provided the same clinical results. However, sometimes tension pneumothorax occurs with the need for additional pleural drainage. The aim of this study was to develop a prognostic model to identify high risk patients intraoperatively and to insert additional pleural drainage to prevent the development of pneumothorax after video-assisted thoracoscopic surgery (VATS) lobectomy. Methods: This was a retrospective multicenter study of patients (registry data was analyzed) who underwent VATS lobectomy via a standardized multiport approach between 2014 and 2022. In all cases, a single drain was used postoperatively. We used a machine learning algorithm and data synthesis to expand patient selection according to Riley's method. A total of 418 cases were analyzed in this study. After determining the prognostically significant factors, we performed a binary logistic regression analysis using reverse step-by-step inclusion of variables according to the Akaike information criterion. After validation of the model by bootstrap (400 iterations) and with the original dataset, a nomogram with a specific point distribution for each risk factor was created. Results: The rate of tension pneumothorax was 4.53% (n=66). The most significant variables associated with the need for additional drainage were adhesions, intraoperative lung suturing, fused interlobar fissure, enlarged intrapulmonary lymph nodes, chronic obstructive pulmonary disease (P<0.001). The C-index of the model was 0.957, the mean absolute calibration error was 0.6%, and the slope of the calibration curve was 0.959. A score of 26 points indicated a 95% risk of postoperative tension pneumothorax. Conclusions: The nomogram achieved good predictive performance for tension pneumothorax after minimally invasive lobectomy. High-risk patients could be identified, and additional drainage may be placed intraoperatively to reduce the risk of lung collapse in the postoperative period.

Indexed as

artificial intelligence (AI)machine learningPleural drainagevideo-assisted thoracoscopic surgery lobectomy (VATS lobectomy)

Identifiers

PMID39444849
PMCPMC11494568

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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.