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Higuchi S, Matsugaki R, Tomisaki I, Fushimi K, Matsuda S, Saeki S. Effect of Early Postoperative Rehabilitation on Length of Hospital Stay after Robot-assisted Radical Prostatectomy. Prog Rehabil Med 2023; 8:20230023. [PMID: 37534203 PMCID: PMC10391540 DOI: 10.2490/prm.20230023] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2023] [Accepted: 06/28/2023] [Indexed: 08/04/2023] Open
Abstract
Objectives : This study assessed how early postoperative rehabilitation interventions affected the duration of hospital stay in patients with prostate cancer who had radical prostatectomy with robotic assistance. Methods : From the Japanese Diagnosis Procedure Combination database, we extracted case data for patients discharged between April 2014 and March 2020. Patients were recognized by code C61 from the International Classification of Diseases, 10th Edition. We ran a multilevel linear regression analysis to investigate the impact of early rehabilitation on the duration of hospital stay. Results : There were 2151 participants in the trial. In patients with prostate cancer who had resection utilizing robotic-assisted devices, early rehabilitation was related to a substantial decrease in duration of hospital stay (coefficient, -0.86; 95% CI, -1.64 to -0.07; P=0.032). Conclusions : Early postoperative rehabilitation may contribute to shorter hospital stays in patients with prostate cancer at high risk of both postoperative complications and a decline in their ability to perform activities of daily living.
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Affiliation(s)
- Shuto Higuchi
- Department of Rehabilitation Medicine, University of Occupational and Environmental Health, Kitakyushu, Japan
- Department of Rehabilitation Center, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Ryutaro Matsugaki
- Department of Preventive Medicine and Community Health, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Ikko Tomisaki
- Department of Urology, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Kiyohide Fushimi
- Department of Health Policy and Informatics, Tokyo Medical and Dental University Graduate School, Tokyo, Japan
| | - Shinya Matsuda
- Department of Preventive Medicine and Community Health, School of Medicine, University of Occupational and Environmental Health, Kitakyushu, Japan
| | - Satoru Saeki
- Department of Rehabilitation Medicine, University of Occupational and Environmental Health, Kitakyushu, Japan
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Achermann C, Sauer A, Cattaneo M, Walz J, Wyler SF, Kwiatkowski M, Prause LW. Retrospective Evaluation of a Single Surgeon's Learning Curve of Robot-Assisted Radical Cystectomy with Intracorporeal Urinary Diversion via Ileal Conduit. Cancers (Basel) 2023; 15:3799. [PMID: 37568615 PMCID: PMC10416818 DOI: 10.3390/cancers15153799] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2023] [Revised: 07/12/2023] [Accepted: 07/21/2023] [Indexed: 08/13/2023] Open
Abstract
Robot-assisted radical cystectomy with intracorporeal urinary diversion (iRARC) is increasingly being performed instead of open surgery. A criticism of this technique is the long learning curve, but limited data are available on this topic. At our center, the transition from open radical cystectomy (ORC) to iRARC began in May 2017. A retrospective analysis was conducted on the initial 53 cases of robot-assisted cystectomy with intracorporeal urinary diversion via ileal conduit, which were performed by one single surgeon. The patients were divided into four consecutive groups according to the surgeon's increasing experience, and perioperative parameters were analyzed as a surrogate for the learning curve. Over the course of the learning curve, a decline in median operation time from 415 to 361 min (p = 0.02), blood loss from 400 to 200 mL (p = 0.01), and minor complications from 71% to 15% (p = 0.02) was observed. No significant difference in overall and major complications, length of hospital stay, and total lymph node yield was shown. During the initial period of the learning curve, only the less complex cases were operated on using robotic surgery, while the more challenging ones were handled through open surgery. After experience with 28 cases, no more cystectomies were performed through open surgery. This led to an increase in operation time and length of hospital stay, as well as a higher incidence of both minor and overall complications among cases 28-40. After 40 cases, a significant decrease in these parameters was observed again. Our analysis demonstrated that operation time, blood loss, and minor complications decrease with increasing surgical experience in iRARC, while suggesting that technically challenging cases should be operated on after experience with 40 robotic cystectomies.
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Affiliation(s)
- Christof Achermann
- Department of Urology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland
- Department of Urology, University Hospital of Basel, University of Basel, 4001 Basel, Switzerland
| | - Andreas Sauer
- Department of Urology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland
| | - Marco Cattaneo
- Department of Clinical Research, University of Basel, 4001 Basel, Switzerland
| | - Jochen Walz
- Department of Urology, Institut Paoli-Calmettes Cancer Center, 13009 Marseille, France
| | - Stephen F. Wyler
- Department of Urology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland
- Medical Faculty, University of Basel, 4056 Basel, Switzerland
| | - Maciej Kwiatkowski
- Department of Urology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland
- Medical Faculty, University of Basel, 4056 Basel, Switzerland
- Department of Urology, Academic Hospital Braunschweig, 38126 Braunschweig, Germany
| | - Lukas W. Prause
- Department of Urology, Cantonal Hospital Aarau, 5001 Aarau, Switzerland
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Abou Chawareb E, Ayoub CH, Najdi J, Ghoubaira J, El-Hajj A. Preoperative predictors of prolonged length of stay in radical cystectomy: a retrospective study using the American College of Surgeons-National Surgical Quality Improvement Program Dataset. Ther Adv Urol 2023; 15:17562872231191654. [PMID: 37577029 PMCID: PMC10413889 DOI: 10.1177/17562872231191654] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/05/2023] [Accepted: 07/14/2023] [Indexed: 08/15/2023] Open
Abstract
Background Radical cystectomy (RC) is considered a complex procedure that entails significant morbidity and mortality. Objectives We aimed to determine pre-operative patient characteristics that help predict a prolonged length of hospital stay (PLOS) following RC. Design and Methods The American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) database was used to select patients who underwent RC between the years 2011 and 2020. Prolonged length of stay was defined as a hospital stay ⩾9 days. We compared patient demographics, pre-operative labs, surgical characteristics, and medical history between patients with or without PLOS. Multivariable logistic regression models controlling for pre-operative characteristics and propensity score matching for post-operative complications were conducted to control for possible confounders. Results The analysis yielded details of 19,158 RC patients of which 6007 (31%) patients had a PLOS. Patients with PLOS were more likely to have post-operative complications that could serve as predictors for the PLOS rather than their pre-operative characteristics. Hence, we matched our cohort for these complications. After matching, patient pre-operative characteristics that predict PLOS included female gender (Odds Ratio (OR) = 5.91), 10-year increase in age (OR = 1.15), non-White race (OR = 1.98), partially or totally dependent functional health status (OR = 2.86), bleeding disorders (OR = 4.67), congestive heart failure (OR = 1.59), pre-operative transfusion (OR = 3.03), and a 20-min increase in operative time (OR = 1.01) (p < 0.046). Conclusion Patient demographics and pre-operative factors can help predict PLOS in RC patients. These predictors could serve as tools for patient counseling and risk stratification.
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Affiliation(s)
- Elia Abou Chawareb
- Division of Urology, Department of Surgery, American University of Beirut Medical Center, Beirut, Lebanon
| | - Christian Habib Ayoub
- Division of Urology, Department of Surgery, American University of Beirut Medical Center, Beirut, Lebanon
| | - Jad Najdi
- Division of Urology, Department of Surgery, American University of Beirut Medical Center, Beirut, Lebanon
| | - Joseph Ghoubaira
- Department of Radiology Oncology, University of Pittsburgh Medical Center, Pittsburgh, PA, USA
| | - Albert El-Hajj
- Division of Urology, Department of Surgery, American University of Beirut Medical Center, PO Box 11-0236, Riad El Solh, Beirut 1107 2020, Lebanon
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Optimization of Tree-Based Machine Learning Models to Predict the Length of Hospital Stay Using Genetic Algorithm. JOURNAL OF HEALTHCARE ENGINEERING 2023; 2023:9673395. [PMID: 36824405 PMCID: PMC9943622 DOI: 10.1155/2023/9673395] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 09/17/2022] [Revised: 12/01/2022] [Accepted: 01/17/2023] [Indexed: 02/16/2023]
Abstract
The length of hospital stay (LOS) is a significant indicator of the quality of patient care, hospital efficiency, and operational resilience. Considering the importance of LOS in hospital resource management, this research aims to improve the accuracy of LOS prediction using hyperparameter optimization (HPO). Expert physicians and related studies were reviewed to determine the variables affecting LOS. The electronic medical records of 200 patients in the department of internal medicine of a hospital in Iran were collected randomly. As the performance of machine learning (ML) models can vary based on the characteristics of the features, several models were applied and evaluated in this study. In particular, k-nearest neighbors (KNN), multivariate regression, decision tree (DT), random forest (RF), artificial neural network (ANN), and XGBoost have been evaluated and improved. The genetic algorithm (GA) was applied to optimize the tree-based models. In addition, the dummy coding technique, sometimes called the One-Hot encoding, was used to encode categorical features to increase prediction accuracy. Compared with other algorithms, the XGBoost model optimized by GA (XGB_GA) achieved higher accuracy and better prediction performance. The mean and median of absolute errors in the test dataset for this model were 1.54 and 1.14 days, respectively. In other words, the XGB_GA model reduced the mean absolute error by 37%, which is beneficial in the reliable design of a clinical decision support system.
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Ayoub CH, Dakroub A, El-Asmar JM, Ali AH, Beaini H, Abdulfattah S, El Hajj A. Preoperative MELD score predicts mortality and adverse outcomes following radical cystectomy: analysis of American College of Surgeons National Surgical Quality Improvement Program. Ther Adv Urol 2022; 14:17562872221135944. [PMID: 36407007 PMCID: PMC9669693 DOI: 10.1177/17562872221135944] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/28/2022] [Accepted: 10/12/2022] [Indexed: 08/15/2023] Open
Abstract
BACKGROUND The model for end-stage liver disease (MELD) has been widely used to predict the mortality and morbidity of various surgical procedures. OBJECTIVES We aimed to correlate a high preoperative MELD score with adverse 30-day postoperative complications following radical cystectomy. DESIGN AND METHODS Patients who underwent elective, non-emergency radical cystectomy were identified from the American College of Surgeons-National Surgical Quality Improvement Program (ACS-NSQIP) database from 2005 to 2017. Patients were categorized according to a calculated MELD score. The primary outcomes of this study were 30-day postoperative mortality, morbidity, and length of hospital stay following radical cystectomy. For further sensitivity analysis, propensity score matching was used to yield a total of 1387 matched pairs and primary outcomes were also assessed in the matched cohort. RESULTS Compared with patients with a MELD < 10, those with MELD ⩾ 10 had significantly higher rates of mortality [odds ratio (OR) = 1.71, p = 0.004], major complications (OR = 1.42, p < 0.001), and prolonged hospital stay (OR = 1.29, p < 0.001) on multivariate analysis. Following risk-adjustment for race, propensity-matched groups revealed that patients with MELD score ⩾ 10 were significantly associated with higher mortality (OR = 1.85, p = 0.008), major complications (OR = 1.34, p < 0.001), yet similar length of hospital stay (OR = 1.17, p = 0.072). CONCLUSION MELD score ⩾ 10 is associated with higher mortality and morbidity in patients undergoing radical cystectomy compared with lower MELD scores. Risk-stratification using MELD score may assist clinicians in identifying high-risk patients to provide adequate preoperative counseling, optimize perioperative conditions, and even consider nonsurgical alternatives.
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Affiliation(s)
- Christian Habib Ayoub
- Division of Urology, Department of Surgery,
American University of Beirut Medical Center, Beirut, Lebanon
| | - Ali Dakroub
- American University of Beirut Medical School,
American University of Beirut, Beirut, Lebanon
| | - Jose M. El-Asmar
- Division of Urology, Department of Surgery,
American University of Beirut Medical Center, Beirut, Lebanon
| | - Adel Hajj Ali
- Cleveland Clinic, Heart, Vascular &
Thoracic Institute, Cleveland, Ohio, USA
| | - Hadi Beaini
- American University of Beirut Medical School,
American University of Beirut, Beirut, Lebanon
| | - Suhaib Abdulfattah
- American University of Beirut Medical School,
American University of Beirut, Beirut, Lebanon
| | - Albert El Hajj
- Division of Urology, Department of Surgery,
American University of Beirut Medical Center, PO BOX: 11-0236, Riad El Solh,
Beirut 1107 2020, Lebanon
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Lequertier V, Wang T, Fondrevelle J, Augusto V, Duclos A. Hospital Length of Stay Prediction Methods: A Systematic Review. Med Care 2021; 59:929-938. [PMID: 34310455 DOI: 10.1097/mlr.0000000000001596] [Citation(s) in RCA: 15] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
Abstract
OBJECTIVE This systematic review sought to establish a picture of length of stay (LOS) prediction methods based on available hospital data and study protocols designed to measure their performance. MATERIALS AND METHODS An English literature search was done relative to hospital LOS prediction from 1972 to September 2019 according to the PRISMA guidelines. Articles were retrieved from PubMed, ScienceDirect, and arXiv databases. Information were extracted from the included papers according to a standardized assessment of population setting and study sample, data sources and input variables, LOS prediction methods, validation study design, and performance evaluation metrics. RESULTS Among 74 selected articles, 98.6% (73/74) used patients' data to predict LOS; 27.0% (20/74) used temporal data; and 21.6% (16/74) used the data about hospitals. Overall, regressions were the most popular prediction methods (64.9%, 48/74), followed by machine learning (20.3%, 15/74) and deep learning (17.6%, 13/74). Regarding validation design, 35.1% (26/74) did not use a test set, whereas 47.3% (35/74) used a separate test set, and 17.6% (13/74) used cross-validation. The most used performance metrics were R2 (47.3%, 35/74), mean squared (or absolute) error (24.4%, 18/74), and the accuracy (14.9%, 11/74). Over the last decade, machine learning and deep learning methods became more popular (P=0.016), and test sets and cross-validation got more and more used (P=0.014). CONCLUSIONS Methods to predict LOS are more and more elaborate and the assessment of their validity is increasingly rigorous. Reducing heterogeneity in how these methods are used and reported is key to transparency on their performance.
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Affiliation(s)
- Vincent Lequertier
- Research on Healthcare Performance (RESHAPE), Université Claude Bernard Lyon 1, INSERM U1290
- Health Data Department, Lyon University Hospital, Lyon
- Univ Lyon, INSA Lyon, Université Claude Bernard Lyon 1, Univ Lumière Lyon 2, DISP, EA4570, 69621 Villeurbanne, France
| | - Tao Wang
- University of Lyon, INSA Lyon, Université Claude Bernard Lyon 1, Univ Lumière Lyon 2, UJM-Saint-Etienne, Decision and Information Systems for Production systems (DISP), Villeurbanne Cedex
| | - Julien Fondrevelle
- Univ Lyon, INSA Lyon, Université Claude Bernard Lyon 1, Univ Lumière Lyon 2, DISP, EA4570, 69621 Villeurbanne, France
| | - Vincent Augusto
- Mines Saint-Etienne, University of Clermont Auvergne, CNRS, UMR 6158 LIMOS, Centre CIS, Saint-Etienne, France
| | - Antoine Duclos
- Research on Healthcare Performance (RESHAPE), Université Claude Bernard Lyon 1, INSERM U1290
- Health Data Department, Lyon University Hospital, Lyon
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