A retrospective study on influencing factors of postoperative hospital stay and development of a predictive scoring model for elderly patients (≥70 years) with colorectal cancer undergoing laparoscopic radical resection.
1/5 보강
PICO 자동 추출 (휴리스틱, conf 2/4)
유사 논문P · Population 대상 환자/모집단
환자: colorectal cancer (CRC) undergoing laparoscopic radical resection is increasing annually
I · Intervention 중재 / 시술
laparoscopic radical resection at our hospital from August 2024 to September 2025 were retrospectively included
C · Comparison 대조 / 비교
추출되지 않음
O · Outcome 결과 / 결론
추출되지 않음
[BACKGROUND] With the accelerating global population aging, the proportion of elderly patients with colorectal cancer (CRC) undergoing laparoscopic radical resection is increasing annually.
APA
Zhou J, He D, et al. (2025). A retrospective study on influencing factors of postoperative hospital stay and development of a predictive scoring model for elderly patients (≥70 years) with colorectal cancer undergoing laparoscopic radical resection.. Frontiers in surgery, 12, 1709437. https://doi.org/10.3389/fsurg.2025.1709437
MLA
Zhou J, et al.. "A retrospective study on influencing factors of postoperative hospital stay and development of a predictive scoring model for elderly patients (≥70 years) with colorectal cancer undergoing laparoscopic radical resection.." Frontiers in surgery, vol. 12, 2025, pp. 1709437.
PMID
41394175 ↗
Abstract 한글 요약
[BACKGROUND] With the accelerating global population aging, the proportion of elderly patients with colorectal cancer (CRC) undergoing laparoscopic radical resection is increasing annually. However, significant individual variations in postoperative hospital stay exist, and convenient clinical prediction tools remain lacking. This study aimed to develop and validate a simplified predictive scoring model for postoperative hospital stay in elderly CRC patients after laparoscopic radical resection.
[MATERIALS AND METHODS] A total of 205 elderly CRC patients (≥70 years) who underwent laparoscopic radical resection at our hospital from August 2024 to September 2025 were retrospectively included. Baseline characteristics (age, sex, BMI, comorbidities), tumor indicators (location, TNM stage), surgical parameters (operative time, blood loss, stoma creation), preoperative and postoperative blood markers (albumin, hemoglobin, direct bilirubin), perioperative assessments (ASA classification), and postoperative outcomes (30-day complications, hospital stay) were collected. Postoperative hospital stay (excluding delays due to non-medical factors) served as the primary outcome. Univariate linear regression identified potential influencing factors, and multivariate linear regression determined independent risk factors. A predictive scoring model was constructed based on independent factors, with efficacy validated using the coefficient of determination ( ) and root mean square error (RMSE). Risk stratification was performed to analyze differences in hospital stay across scoring tiers.
[RESULTS] The mean postoperative hospital stay was 16.6 ± 5.0 days (range: 9-42 days). multivariate linear regression analysis revealed that 30-day postoperative complications were an independent risk factor for prolonged hospital stay ( = 7.689, < 0.001). A simplified scoring model was developed: postoperative complications (present = 3 points, absent = 0 points), ≥2 comorbidities (yes = 1 point, no = 0 points), and operative time >180 min (yes = 1 point, no = 0 points), yielding a total score range of 0-5 points. Risk stratification showed: low-risk group (0 points) had an expected stay of 14.8 ± 3.2 days (15% of patients), medium-risk group (1-2 points) 16.0 ± 4.0 days (60%), and high-risk group (3-5 points) 22.5 ± 6.8 days (25%).
[CONCLUSION] The simplified scoring model developed in this study effectively predicts postoperative hospital stay in elderly CRC patients undergoing laparoscopic radical resection, providing a practical tool for clinical risk stratification, early intervention, and optimization of healthcare resources.
[MATERIALS AND METHODS] A total of 205 elderly CRC patients (≥70 years) who underwent laparoscopic radical resection at our hospital from August 2024 to September 2025 were retrospectively included. Baseline characteristics (age, sex, BMI, comorbidities), tumor indicators (location, TNM stage), surgical parameters (operative time, blood loss, stoma creation), preoperative and postoperative blood markers (albumin, hemoglobin, direct bilirubin), perioperative assessments (ASA classification), and postoperative outcomes (30-day complications, hospital stay) were collected. Postoperative hospital stay (excluding delays due to non-medical factors) served as the primary outcome. Univariate linear regression identified potential influencing factors, and multivariate linear regression determined independent risk factors. A predictive scoring model was constructed based on independent factors, with efficacy validated using the coefficient of determination ( ) and root mean square error (RMSE). Risk stratification was performed to analyze differences in hospital stay across scoring tiers.
[RESULTS] The mean postoperative hospital stay was 16.6 ± 5.0 days (range: 9-42 days). multivariate linear regression analysis revealed that 30-day postoperative complications were an independent risk factor for prolonged hospital stay ( = 7.689, < 0.001). A simplified scoring model was developed: postoperative complications (present = 3 points, absent = 0 points), ≥2 comorbidities (yes = 1 point, no = 0 points), and operative time >180 min (yes = 1 point, no = 0 points), yielding a total score range of 0-5 points. Risk stratification showed: low-risk group (0 points) had an expected stay of 14.8 ± 3.2 days (15% of patients), medium-risk group (1-2 points) 16.0 ± 4.0 days (60%), and high-risk group (3-5 points) 22.5 ± 6.8 days (25%).
[CONCLUSION] The simplified scoring model developed in this study effectively predicts postoperative hospital stay in elderly CRC patients undergoing laparoscopic radical resection, providing a practical tool for clinical risk stratification, early intervention, and optimization of healthcare resources.
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