Background: Transfer to the ICU is common following non-cardiac surgeries, including radical colorectal cancer (CRC) resection. Understanding the judicious utilization of costly ICU medical resources and supportive postoperative care is crucial. This study aimed to construct and validate a nomogram for predicting the need for mandatory ICU admission immediately following radical CRC resection.
Methods: Retrospective analysis was conducted on data from 1003 patients who underwent radical or palliative surgery for CRC at Ningxia Medical University General Hospital from August 2020 to April 2022. Patients were randomly assigned to training and validation cohorts in a 7:3 ratio. Independent predictors were identified using the least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression in the training cohort to construct the nomogram. An online prediction tool was developed for clinical use. The nomogram's calibration and discriminative performance were assessed in both cohorts, and its clinical utility was evaluated through decision curve analysis (DCA).
Results: The final predictive model comprised age (P = 0.003, odds ratio [OR] 3.623, 95% confidence interval [CI] 1.535-8.551); nutritional risk screening 2002 (NRS2002) (P = 0.000, OR 6.129, 95% CI 2.920-12.863); serum albumin (ALB) (P = 0.013, OR 0.921, 95% CI 0.863-0.982); atrial fibrillation (P = 0.000, OR 20.017, 95% CI 4.191-95.609); chronic obstructive pulmonary disease (COPD) (P = 0.009, OR 8.151, 95% CI 1.674-39.676); forced expiratory volume in 1 s / Forced vital capacity (FEV1/FVC) (P = 0.040, OR 0.966, 95% CI 0.935-0.998); and surgical method (P = 0.024, OR 0.425, 95% CI 0.202-0.891). The area under the curve was 0.865, and the consistency index was 0.367. The Hosmer-Lemeshow test indicated excellent model fit (P = 0.367). The calibration curve closely approximated the ideal diagonal line. DCA showed a significant net benefit of the predictive model for postoperative ICU admission.
Conclusion: Predictors of ICU admission following radical CRC resection include age, preoperative serum albumin level, nutritional risk screening, atrial fibrillation, COPD, FEV1/FVC, and surgical route. The predictive nomogram and online tool support clinical decision-making for postoperative ICU admission in patients undergoing radical CRC surgery.
Trial Registration: Despite the retrospective nature of this study, we have proactively registered it with the Chinese Clinical Trial Registry. The registration number is ChiCTR2200062210, and the date of registration is 29/07/2022.
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http://dx.doi.org/10.1186/s12871-024-02598-3 | DOI Listing |
Clin Infect Dis
January 2025
Charité - Universitätsmedizin Berlin, corporate member of Freie Universität Berlin and Humboldt-Universität zu Berlin, Department of Infectious Diseases, Respiratory Medicine and Critical Care, Berlin, Germany.
Background: Existing risk evaluation tools underperform in predicting intensive care unit (ICU) admission for patients with the Coronavirus Disease 2019 (COVID-19). This study aimed to develop and evaluate an accurate and calculator-free clinical tool for predicting ICU admission at emergency room (ER) presentation.
Methods: Data from patients with COVID-19 in a nationwide German cohort (March 2020-January 2023) were analyzed.
J Chin Med Assoc
November 2024
Division of Trauma Surgery, Department of Emergency, Kaohsiung Veterans General Hospital, Kaohsiung, Taiwan, ROC.
Background: Trauma is consistently among the top ten causes of death worldwide. The aging population, constituting 15.21% of adults aged over 65 in Taiwan as of November 2019, has significantly impacted healthcare expenditures.
View Article and Find Full Text PDFAdv Skin Wound Care
January 2025
Öznur Tiryaki, PhD, RN, is Associate Professor, Faculty of Health Sciences, Department of Midwifery, Sakarya University, Sakarya, Turkey. Hamide Zengin, PhD, RN, is Associate Professor, Faculty of Health Science, Department of Pediatric Nursing, Eskişehir Osmangazi University, Eskişehir, Turkey. Also at Sakarya University, Nursan Çınar, PhD, RN, is Professor, Faculty of Health Sciences, Department of Pediatric Nursing; Meltem Karabay, MD, is Associate Professor, Faculty of Medicine, Research and Training Hospital of Sakarya, Division of Neonatology, Department of Pediatrics; İbrahim Caner, MD, is Professor, Faculty of Medicine, Research and Training Hospital of Sakarya, Division of Neonatology, Department of Pediatrics; and Ertuğrul Güçlü, MD, is Professor, Faculty of Medicine, Department of Infectious Diseases and Clinical Microbiology.
Objective: To determine the effects of sunflower seed oil and coconut oil on the skin integrity and weight gain of preterm infants in the neonatal ICU.
Methods: In this randomized controlled trial, 66 preterm neonates (34-37 weeks' gestation) in the neonatal ICU of a training and research hospital were equally divided into three groups: sunflower seed oil, coconut oil, and control. The weights of neonates in all three groups were measured at admission to the neonatal ICU, at discharge, and at 1 month postdischarge.
Crit Care Explor
January 2025
Department of Pediatrics, Johns Hopkins University, Baltimore, MD.
Objectives: Exploiting the complete blood count (CBC) with differential (CBC-diff) for early sepsis detection has practical value for emergency department (ED) care, especially for those without obvious presentations. The objective of this study was to develop the CBC Sepsis Index (CBC-SI) that incorporates monocyte distribution width (MDW) to enhance rapid sepsis screening.
Design: A retrospective observational study.
Med Care
February 2025
University of Pennsylvania School of Nursing, NewCourtland Center for Transitions and Health, Philadelphia, PA.
Objective: To examine the characteristics and risk factors associated with 30-day readmissions, including the impact of home health care (HHC), among older sepsis survivors transitioning from hospital to home.
Research Design: Retrospective cohort study of the Medical Information Mart for Intensive Care (MIMIC)-IV data (2008-2019), using generalized estimating equations (GEE) models adjusting for patient sociodemographic and clinical characteristics.
Subjects: Sepsis admission episodes with in-hospital stays, aged over 65, and discharged home with or without HHC were included.
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