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Liu SH, Ling K, Loyst RA, Al-Humadi S, Komatsu DE, Wang ED. Preoperative thrombocytopenia and thrombocytosis predict complications after arthroscopic rotator cuff repair. JSES REVIEWS, REPORTS, AND TECHNIQUES 2024; 4:48-52. [PMID: 38323198 PMCID: PMC10840563 DOI: 10.1016/j.xrrt.2023.09.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/08/2024]
Abstract
Background The purpose of this study was to investigate the association between preoperative platelet count and 30-day postoperative complications following arthroscopic rotator cuff repair (aRCR). Methods The American College of Surgeons National Surgical Quality Improvement database was queried for all patients who underwent aRCR between 2015 and 2021. The study population was divided into 5 groups based on preoperative platelet count: normal (200-450k, reference cohort), low-normal (150-200k), mild thrombocytopenia (100-150k), moderate-to-severe thrombocytopenia (<100k), and thrombocytosis (>450k). Thirty-day postoperative complications following aRCR were collected. Multivariate logistic regression analysis was conducted to investigate the relationship between preoperative platelet counts and postoperative complications. Results 24,779 patients were included in this study: 18,697 (75.5%) in the normal group, 4730 (19.1%) in the low-normal group, 1012 (4.1%) in the mild thrombocytopenia group, 171 (0.7%) in the moderate-to-severe thrombocytopenia group, and 169 (0.7%) in the thrombocytosis group. Low-normal platelets were an independent predictor of urinary tract infection (odds ratio [OR] 2.06, 95% confidence interval [CI] 1.12-3.77; P = .020). Mild thrombocytopenia was not an independent predictor of any complications. Moderate-to-severe thrombocytopenia was an independent predictor of sepsis (OR 9.39, 95% CI 1.48-59.47; P = .017), pneumonia (OR 6.62, 95% CI 1.32-33.24; P = .022), and nonhome discharge (OR 3.34, 95% CI 1.20-9.25; P = .021). Thrombocytosis was an independent predictor of urinary tract infection (OR 4.91, 95% CI 1.16-20.78; P = .030). Conclusion Abnormal preoperative platelet counts, both low and high, were independent risk factors for 30-day postoperative complications following aRCR.
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Affiliation(s)
- Steven H. Liu
- Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Kenny Ling
- Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Rachel A. Loyst
- Renaissance School of Medicine, Stony Brook University, Stony Brook, NY, USA
| | - Samer Al-Humadi
- Department of Orthopaedics, Stony Brook University, Stony Brook, NY, USA
| | - David E. Komatsu
- Department of Orthopaedics, Stony Brook University, Stony Brook, NY, USA
| | - Edward D. Wang
- Department of Orthopaedics, Stony Brook University, Stony Brook, NY, USA
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Does 3D Printing-Assisted Acetabular or Pelvic Fracture Surgery Shorten Hospitalization Durations among Older Adults? J Pers Med 2022; 12:jpm12020189. [PMID: 35207678 PMCID: PMC8876197 DOI: 10.3390/jpm12020189] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2022] [Revised: 01/18/2022] [Accepted: 01/20/2022] [Indexed: 12/03/2022] Open
Abstract
Acetabular or anterior pelvic ring fractures are rare but extremely complicated and challenging injuries for orthopedic trauma surgeons. Three-dimensional (3D) printing technology is widely used in the management of these two fracture types for surgical benefits. Our study aimed to explore whether 3D printing-assisted acetabular or pelvic surgery is beneficial in terms of shortening the length of hospital stay (LHS) and intensive care unit (ICU) stay (ICU LS) for older patients. This retrospective study included two groups of 76 participants over 60 years old who underwent operations with (n = 41) or without (n = 35) guidance by 3D printing. The Mann–Whitney U test was used to analyze continuous variables. Chi-square analysis was applied for categorical variables. Univariable and multivariable linear regression models were used to analyze the factors associated with LHS. The median LHS in the group without 3D printing assistance was 16 (12–21) days, and the median ICU LS was 0 (0–2) days. The median LHS in the group with 3D printing assistance was 17 (12.5–22.5) days, and the median ICU LS was 0 (0–3) days. There was no significant difference in LHS associated with 3D printing assistance vs. that without 3D printing among patients who underwent open reduction and internal fixation for pelvic or acetabular fractures. The LHS positively correlated with the ICU LS whether the operation was 3D printing assisted or not. For fracture surgery in older patients, in addition to the advancement of surgical treatment and techniques, medical teams require more detailed preoperative evaluations, and more personalized medical plans regarding postoperative care to achieve the goals of shortening LHS, reducing healthcare costs, and reducing complication rates.
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Hersh AM, Feghali J, Hung B, Pennington Z, Schilling A, Antar A, Patel J, Ehresman J, Cottrill E, Lubelski D, Elsamadicy AA, Goodwin CR, Lo SFL, Sciubba DM. A Web-Based Calculator for Predicting the Occurrence of Wound Complications, Wound Infection, and Unplanned Reoperation for Wound Complications in Patients Undergoing Surgery for Spinal Metastases. World Neurosurg 2021; 155:e218-e228. [PMID: 34403800 DOI: 10.1016/j.wneu.2021.08.041] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2021] [Revised: 08/08/2021] [Accepted: 08/09/2021] [Indexed: 01/05/2023]
Abstract
BACKGROUND In the present study, we identified the risk factors for wound complications, wound infection, and reoperation for wound complications after spine metastasis surgery and deployed the resultant model as a web-based calculator. METHODS Patients treated at a single comprehensive cancer center during a 7-year period were included. The demographics, pathology, comorbidities, laboratory values, and operative details were collected. Factors with P < 0.15 on univariable regression were entered into multivariable logistic regression to generate predictive models internally validated using 1000 bootstrapped samples. RESULTS Of the 330 patients included, 29 (7.6%) had experienced a surgical site infection. The independent predictive factors for wound-related complications were a higher Charlson comorbidity index (CCI; odds ratio [OR], 1.41 per point; P < 0.01), Karnofsky performance scale score ≤70 (OR, 2.14; P = 0.04), lower platelet count (OR, 0.49 per 105/μL; P < 0.01), revision versus index surgery (OR, 3.10; P = 0.02), and increased incision length (OR, 1.21 per level; P = 0.02). Wound infection was associated with a higher CCI (OR, 1.60 per point; P < 0.01), a lower platelet count (OR, 0.35 per 105/μL; P < 0.01), revision surgery (OR, 4.63; P = 0.01), and a longer incision length (OR, 1.25 per level; P = 0.03). Unplanned reoperation for wound complications was predicted by a higher CCI (OR, 1.39 per point; P = 0.003), prior irradiation (OR, 2.52; P = 0.04), a lower platelet count (OR, 0.57 per 105/μL; P = 0.02), and revision surgery (OR, 3.34; P = 0.03), The optimism-corrected areas under the curve were 0.75, 0.81, and 0.72 for the wound complication, infection, and reoperation models, respectively. CONCLUSIONS Low platelet counts, poorer health status, more invasive surgery, and revision surgery all independently predicted the risk of wound complications, including infection and unplanned reoperation for infection. Validation of the calculators in a prospective study is merited.
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Affiliation(s)
- Andrew M Hersh
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - James Feghali
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Bethany Hung
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Zach Pennington
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; Department of Neurosurgery, Mayo Clinic, Rochester, Minnesota, USA
| | - Andy Schilling
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Albert Antar
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jaimin Patel
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Jeff Ehresman
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; Department of Neurosurgery, Barrow Neurological Institute, Phoenix, Arizona, USA
| | - Ethan Cottrill
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Daniel Lubelski
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA
| | - Aladine A Elsamadicy
- Department of Neurosurgery, Yale University School of Medicine, New Haven, Connecticut, USA
| | - C Rory Goodwin
- Department of Neurosurgery, Duke University Medical Center, Durham, North Carolina, USA
| | - Sheng-Fu Larry Lo
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; Department of Neurosurgery, Zucker School of Medicine at Hofstra, Long Island Jewish Medical Center and North Shore University Hospital, Northwell Health, Manhasset, New York, USA
| | - Daniel M Sciubba
- Department of Neurosurgery, Johns Hopkins University School of Medicine, Baltimore, Maryland, USA; Department of Neurosurgery, Zucker School of Medicine at Hofstra, Long Island Jewish Medical Center and North Shore University Hospital, Northwell Health, Manhasset, New York, USA.
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