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Record W4415903733 · doi:10.1007/s10151-025-03232-1

COVID-19-specific risk factor for early post-appendectomy complications (EPAC) in older patients: a retrospective study

2025· article· en· W4415903733 on OpenAlexaff
Tamer A. A. M. Habeeb, Abdulzahra Hussain, José Bueno‐Lledó, Mariano Giménez, Alberto Aiolfi, Massimo Chiaretti, І. А. Кryvoruchko, Mallikarjuna Manangi, Abd Al-Kareem Elias, Abdelmonem A M Adam, Mohamed A. Gadallah, Saad Mohamed Ali Ahmed, Ahmed Khyrallh, Mohammed H. Alsayed, Esmail Tharwat Kamel Awad, Emad A. Ibrahim, Mohammed Hassan Elshafey, Mohamed Fathy Labib, Mahmoud Hassib Morsi Badawy, Sobhy rezk ahmed Teama, Abdelhafez Seleem, Mohamed Ibrahim Abo Alsaad, Asmaa Ali, Hamdi Elbelkasi, Basma Mohamed, Alaa Abood Al-Wadees, Ahmed K El-Taher, Mohamed Ibrahim Mansour, Mahmoud Abdou Yassin, Ahmed Salah Arafa, Mohamed Lotfy, Baher Atef, Mohamed Elnemr, Mostafa Mohamed Khairy, Abdelfatah H. Abdelwanis, Ahmed Mesbah Abdelaziz, Abdelshafy Mostafa, Tamer Wasefy, Ibrahim A. Heggy

Bibliographic record

VenueTechniques in Coloproctology · 2025
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsVictoria General Hospital
FundersZagazig UniversityScience and Technology Development Fund
KeywordsRetrospective cohort studyAbdominal surgeryRisk factorColorectal surgeryMEDLINE

Abstract

fetched live from OpenAlex

BACKGROUND: The incidence of acute appendicitis in older patients significantly varies from that in younger adults. The coronavirus disease 2019 (COVID-19) pandemic has increased the risk of early post-appendectomy complications (EPAC). This study aimed to investigate the incidence and risk factors associated with EPAC in older patients after appendectomy and to define active COVID-19 infection during surgery as an associated risk factor for EPAC. METHODS: We conducted a retrospective multicenter analysis of older patients aged ≥ 60 years who underwent appendectomy between April 2020 and December 2024. Logistic regression identified the risk factors associated with EPAC. RESULTS: A total of 585 patients aged ≥ 60 years were divided into the EPAC (n = 32) and no EPAC (n = 553) groups. The incidences of EPAC was 5.5% (32/585), including superficial incisional surgical site infections (SSI) (9/32, 28.1%), deep incisional SSI (2/32, 6.3%), organ/space infection (2/32, 6.3%), intra-abdominal abscess (9/32, 28.1%), ileus (2/32, 6.3%), pneumonia (3/32, 9.4%), acute myocardial infraction (MI) (2/32, 6.3%), fecal fistula (2/32, 6.3%), and acute adhesive intestinal obstruction (1/32, 3.1%). Multivariable analysis identified that active COVID-19 infection during surgery (odds ratio (OR) = 25.9; 95% confidence interval (CI) 4.8-139.1; p < 0.001), American Society of Anesthesiologists (ASA) score ≥ II (OR = 4.5; 95% CI 1.2-17.07; p = 0.02), open approach (OR = 30.6; 95% CI 8.1-115.3; p < 0.001), and high-grade appendicitis ≥ IV (OR = 63.06; 95% CI 7.5-526.4; p < 0.001) were significant associated risk factors for EPAC. CONCLUSIONS: The incidence of EPAC in older patients after appendectomy is 5.5%. Active COVID-19 infection during surgery is strongly associated with an increased risk of EPAC. COVID-19 should be considered in perioperative risk assessment of EPAC. TRIAL REGISTRATION: This study was registered as a clinical trial (NCT06787573). Retrospectively registered.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.004
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.013
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.049
GPT teacher head0.434
Teacher spread0.384 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations0
Published2025
Admission routes1
Has abstractyes

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