Exploratory Analysis of Factors Affecting 30-Day, 90-Day, and 1-Year Readmission After Surgical Treatment of Primary Spinal Infection in Adults

<b>Background</b><i>:</i> Unplanned hospital readmission after surgical treatment of primary spinal infections (PSIs) represents a major clinical and economic burden. Despite the fact that advances in surgical and antimicrobial management have been made, risk stratification for early and late readmissions remains poorly defined. <b>Objective</b>: We aimed to explore the clinical, microbiological, and perioperative characteristics potentially associated with 30-day, 90-day, and 1-year unplanned readmissions following the surgical treatment of PSIs in adult patients. <b>Methods</b>: A retrospective cohort study was performed that included adult patients who underwent surgery for primary spinal infections between January 2017 and December 2023 at our tertiary referral center. Demographics, comorbidities, laboratory parameters, microbiological profiles, and postoperative outcomes were analyzed. Associations between candidate variables and readmission were explored using univariate statistical analyses; multivariable modeling was not performed due to the low number of readmission events. <b>Results</b>: In total, seventy-nine patients (mean age 62.2 ± 12.7 years; 38% female) were included. The in-hospital mortality rate was 5.1%; at 1-year follow-up, 10.3% of patients were readmitted and 5.9% required reoperation; and <i>Staphylococcus aureus</i> was the most common isolated pathogen. No independent variables demonstrated a statistically significant association with readmission. However, trends toward higher readmission were observed in patients with liver disease, hypoalbuminemia, and postoperative transfusion. <b>Conclusions</b>: In this exploratory single-center cohort, the low number of readmission events limited statistical power and precluded adjusted modeling. Univariate analyses did not identify statistically significant associations between the evaluated variables and 30-day, 90-day, or 1-year readmission; therefore, the results should be interpreted cautiously as hypothesis-generating. Larger prospective multicenter studies with adequate event counts are needed to support adjusted risk stratification approaches. Until such tools are available, close postoperative follow-up across all PSI patients is necessary.

Authors

Institutions

Publication Details

Journal
Journal of Clinical Medicine
Published
2026-02-19
DOI
https://doi.org/10.3390/jcm15041600
Primary Topic
Infectious Diseases and Tuberculosis
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Exploratory Analysis of Factors Affecting 30-Day, 90-Day, and 1-Year Readmission After Surgical Treatment of Primary Spinal Infection in Adults

Hasan Kâmil Sucu, Tuna Demırdal, I. Sevin, Selin Bozdağ et al.
Journal of Clinical Medicine
Infectious Diseases and Tuberculosis
article

Exploratory Analysis of Factors Affecting 30-Day, 90-Day, and 1-Year Readmission After Surgical Treatment of Primary Spinal Infection in Adults

Hasan Kâmil Sucu, Tuna Demırdal, I. Sevin, Selin Bozdağ, Onur Davut Dağ, Ibrahim Eralp Sevin, Pelin Pugar
article en

Abstract

<b>Background</b><i>:</i> Unplanned hospital readmission after surgical treatment of primary spinal infections (PSIs) represents a major clinical and economic burden. Despite the fact that advances in surgical and antimicrobial management have been made, risk stratification for early and late readmissions remains poorly defined. <b>Objective</b>: We aimed to explore the clinical, microbiological, and perioperative characteristics potentially associated with 30-day, 90-day, and 1-year unplanned readmissions following the surgical treatment of PSIs in adult patients. <b>Methods</b>: A retrospective cohort study was performed that included adult patients who underwent surgery for primary spinal infections between January 2017 and December 2023 at our tertiary referral center. Demographics, comorbidities, laboratory parameters, microbiological profiles, and postoperative outcomes were analyzed. Associations between candidate variables and readmission were explored using univariate statistical analyses; multivariable modeling was not performed due to the low number of readmission events. <b>Results</b>: In total, seventy-nine patients (mean age 62.2 ± 12.7 years; 38% female) were included. The in-hospital mortality rate was 5.1%; at 1-year follow-up, 10.3% of patients were readmitted and 5.9% required reoperation; and <i>Staphylococcus aureus</i> was the most common isolated pathogen. No independent variables demonstrated a statistically significant association with readmission. However, trends toward higher readmission were observed in patients with liver disease, hypoalbuminemia, and postoperative transfusion. <b>Conclusions</b>: In this exploratory single-center cohort, the low number of readmission events limited statistical power and precluded adjusted modeling. Univariate analyses did not identify statistically significant associations between the evaluated variables and 30-day, 90-day, or 1-year readmission; therefore, the results should be interpreted cautiously as hypothesis-generating. Larger prospective multicenter studies with adequate event counts are needed to support adjusted risk stratification approaches. Until such tools are available, close postoperative follow-up across all PSI patients is necessary.

Journal of Clinical MedicineVol. 15(4)
Izmir Kâtip Çelebi University (TR)
Good health and well-being
Openalex Percentile: Top 50%
Infectious Diseases and Tuberculosis
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.