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Ochs V, Tobler A, Wolleb J, Bieder F, Saad B, Enodien B, Fischer LE, Honaker MD, Drews S, Rosenblum I, Stoll R, Probst P, Müller MK, Lavanchy JL, Taha-Mehlitz S, Müller BP, Rosenberg R, Frey DM, Cattin PC, Taha A. Development of predictive model for predicting postoperative BMI and optimize bariatric surgery: a single center pilot study. Surg Obes Relat Dis 2024:S1550-7289(24)00680-4. [PMID: 39117560 DOI: 10.1016/j.soard.2024.06.012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 06/04/2024] [Accepted: 06/30/2024] [Indexed: 08/10/2024]
Abstract
BACKGROUND The pilot study addresses the challenge of predicting postoperative outcomes, particularly body mass index (BMI) trajectories, following bariatric surgery. The complexity of this task makes preoperative personalized obesity treatment challenging. OBJECTIVES To develop and validate sophisticated machine learning (ML) algorithms capable of accurately forecasting BMI reductions up to 5 years following bariatric surgery aiming to enhance planning and postoperative care. The secondary goal involves the creation of an accessible web-based calculator for healthcare professionals. This is the first article that compares these methods in BMI prediction. SETTING The study was carried out from January 2012 to December 2021 at GZOAdipositas Surgery Center, Switzerland. Preoperatively, data for 1004 patients were available. Six months postoperatively, data for 1098 patients were available. For the time points 12 months, 18 months, 2 years, 3 years, 4 years, and 5 years the following number of follow-ups were available: 971, 898, 829, 693, 589, and 453. METHODS We conducted a comprehensive retrospective review of adult patients who underwent bariatric surgery (Roux-en-Y gastric bypass or sleeve gastrectomy), focusing on individuals with preoperative and postoperative data. Patients with certain preoperative conditions and those lacking complete data sets were excluded. Additional exclusion criteria were patients with incomplete data or follow-up, pregnancy during the follow-up period, or preoperative BMI ≤30 kg/m2. RESULTS This study analyzed 1104 patients, with 883 used for model training and 221 for final evaluation, the study achieved reliable predictive capabilities, as measured by root mean square error (RMSE). The RMSE values for three tasks were 2.17 (predicting next BMI value), 1.71 (predicting BMI at any future time point), and 3.49 (predicting the 5-year postoperative BMI curve). These results were showcased through a web application, enhancing clinical accessibility and decision-making. CONCLUSION This study highlights the potential of ML to significantly improve bariatric surgical outcomes and overall healthcare efficiency through precise BMI predictions and personalized intervention strategies.
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Affiliation(s)
- Vincent Ochs
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Anja Tobler
- Department of Surgery, GZO-Hospital, Wetzikon, Switzerland
| | - Julia Wolleb
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Florentin Bieder
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Baraa Saad
- Faculty of Medicine, St. George's University of London, London, UK
| | - Bassey Enodien
- Department of Surgery, GZO-Hospital, Wetzikon, Switzerland
| | - Laura E Fischer
- Department of Surgery, University of Oklahoma Health Sciences Center, Oklahoma City, Oklahoma
| | - Michael D Honaker
- Department of Surgery, East Carolina University, Brody School of Medicine, Greenville, North Carolina
| | - Susanne Drews
- Department of Surgery, Centre for Gastrointestinal Diseases, Cantonal Hospital Basel-Landschaft, Liestal, Switzerland
| | - Ilan Rosenblum
- Department of Surgery, Centre for Gastrointestinal Diseases, Cantonal Hospital Basel-Landschaft, Liestal, Switzerland
| | - Reinhard Stoll
- Department of Surgery, Centre for Gastrointestinal Diseases, Cantonal Hospital Basel-Landschaft, Liestal, Switzerland
| | - Pascal Probst
- Department of Surgery, Cantonal Hospital Thurgau, Frauenfeld, Switzerland
| | - Markus K Müller
- Department of Surgery, Cantonal Hospital Thurgau, Frauenfeld, Switzerland
| | - Joël L Lavanchy
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland; Clarunis, Department of Visceral Surgery, University Center for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, Basel, Switzerland
| | - Stephanie Taha-Mehlitz
- Clarunis, Department of Visceral Surgery, University Center for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, Basel, Switzerland
| | - Beat P Müller
- Clarunis, Department of Visceral Surgery, University Center for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, Basel, Switzerland
| | - Robert Rosenberg
- Department of Surgery, Centre for Gastrointestinal Diseases, Cantonal Hospital Basel-Landschaft, Liestal, Switzerland
| | - Daniel M Frey
- Department of Surgery, GZO-Hospital, Wetzikon, Switzerland; Faculty of Medicine, University of Basel, Basel, Switzerland
| | - Philippe C Cattin
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Anas Taha
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland; Department of Surgery, East Carolina University, Brody School of Medicine, Greenville, North Carolina; Department of Surgery, Centre for Gastrointestinal Diseases, Cantonal Hospital Basel-Landschaft, Liestal, Switzerland.
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Giordano S, Salval A, Oranges CM. Concomitant Panniculectomy in Abdominal Wall Reconstruction: A Narrative Review Focusing on Obese Patients. Clin Pract 2024; 14:653-660. [PMID: 38666810 PMCID: PMC11048991 DOI: 10.3390/clinpract14020052] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2023] [Revised: 03/24/2024] [Accepted: 04/17/2024] [Indexed: 04/28/2024] Open
Abstract
The global prevalence of obesity continues to rise, contributing to an increased frequency of abdominal wall reconstruction procedures, particularly ventral hernia repairs, in individuals with elevated body mass indexes. Undertaking these operations in obese patients poses inherent challenges. This review focuses on the current literature in this area, with special attention to the impact of concomitant panniculectomy. Obese individuals undergoing abdominal wall reconstruction face elevated rates of wound healing complications and hernia recurrence. The inclusion of concurrent panniculectomy heightens the risk of surgical site occurrences but does not significantly influence hernia recurrence rates. While this combined approach can be executed in obese patients, caution is warranted, due to the higher risk of complications. Physicians should carefully balance and communicate the potential risks, especially regarding the increased likelihood of wound healing complications. Acknowledging these factors is crucial in shared decision making and ensuring optimal patient outcomes in the context of abdominal wall reconstruction and related procedures in the obese population.
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Affiliation(s)
- Salvatore Giordano
- Department of Plastic and General Surgery, Turku University Hospital, University of Turku, 20014 Turku, Finland;
| | - Andre’ Salval
- Department of Plastic and General Surgery, Turku University Hospital, University of Turku, 20014 Turku, Finland;
| | - Carlo Maria Oranges
- Department of Plastic, Reconstructive and Aesthetic Surgery, Geneva University Hospitals, University of Geneva, 1205 Geneva, Switzerland;
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Dowgiałło-Gornowicz N, Lech P, Katkowski B, Walędziak M, Proczko-Stepaniak M, Szymański M, Karpińska I, Major P. Risk factors for bariatric surgery in patients over 65 years of age-a multicenter retrospective cohort study. Langenbecks Arch Surg 2024; 409:115. [PMID: 38589572 PMCID: PMC11001652 DOI: 10.1007/s00423-024-03304-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2023] [Accepted: 04/03/2024] [Indexed: 04/10/2024]
Abstract
PURPOSE Societies are aging, life expectancy is increasing, and as a result, the percentage of elderly people in the population is constantly increasing. When qualifying patients over 65 years of age for bariatric surgery, the benefits and risks should be carefully assessed. Weighing risk factors against each other to improve the quality of life and better control of obesity-related diseases. The study aimed to determine risk factors for bariatric surgery among patients over 65 years of age. METHODS A multicenter, retrospective analysis of patients undergoing laparoscopic bariatric procedures from 2008 to 2022. The patients were divided into two groups: complicated (C) and uncomplicated (UC). Uni- and multivariate logistic regression analysis was performed to obtain significant, independent risk factors. RESULTS There were 20 (7.0%) patients in C group and 264 (93.0%) patients in UC group. The most common complication was intraperitoneal bleeding (8, 2.8). There was no postoperative mortality. The mean follow-up was 47.5 months. In a multivariate logistic regression analysis, length of stay and %EWL significantly corresponded to general complications (OR 1.173, OR 1.020). A higher weight loss before surgery lowered the risk for hemorrhagic events after surgery (OR 0.889). A longer length of stay corresponded to leak after surgery (OR 1.175). CONCLUSIONS Bariatric and metabolic surgery appears to be a safe method of obesity treatment in patients over 65 years of age. The most common complication was intraperitoneal bleeding. A prolonged hospital stay may increase the risk of leakage, while a higher weight loss before the surgery may lower the risk of bleeding.
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Affiliation(s)
- Natalia Dowgiałło-Gornowicz
- Department of General, Minimally Invasive and Elderly Surgery, Collegium Medicum, University of Warmia and Mazury, 10-045, Olsztyn, Poland.
| | - Paweł Lech
- Department of General, Minimally Invasive and Elderly Surgery, Collegium Medicum, University of Warmia and Mazury, 10-045, Olsztyn, Poland
| | - Bartosz Katkowski
- Department of General and Vascular Surgery, Specialist Medical Center, 57-320, Polanica Zdrój, Poland
| | - Maciej Walędziak
- Department of General, Oncological, Metabolic and Thoracic Surgery, Military Institute of Medicine, 04-141, Warsaw, Poland
| | - Monika Proczko-Stepaniak
- Department of General, Endocrine and Transplant Surgery, Medical University of Gdansk, 80-214, Gdańsk, Poland
| | - Michał Szymański
- Department of General, Endocrine and Transplant Surgery, Medical University of Gdansk, 80-214, Gdańsk, Poland
| | - Izabela Karpińska
- 2nd Department of General Surgery, Jagiellonian University Medical College, 30-688, Cracow, Poland
- Doctoral School of Medical and Health Sciences, Jagiellonian University Medical College, Cracow, Poland
| | - Piotr Major
- 2nd Department of General Surgery, Jagiellonian University Medical College, 30-688, Cracow, Poland
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Wilmington R, Abuawwad M, Holt G, Anderson R, Aldafas R, Awad S, Idris I. The Effects of Preoperative Glycaemic Control (HbA1c) on Bariatric and Metabolic Surgery Outcomes: Data from a Tertiary-Referral Bariatric Centre in the UK. Obes Surg 2024; 34:850-854. [PMID: 38221566 PMCID: PMC10899277 DOI: 10.1007/s11695-023-06964-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/27/2023] [Revised: 11/14/2023] [Accepted: 11/14/2023] [Indexed: 01/16/2024]
Abstract
BACKGROUND Current recommendations advocate the achievement of an optimal glucose control (HbA1c < 69 mmol/mol) prior to elective surgery to reduce risks of peri- and post-operative complications, but the relevance for this glycaemic threshold prior to Bariatric Metabolic Surgery (BMS) following a specialist weight management programme remains unclear. METHODS We undertook a retrospective cohort study of patients with type 2 diabetes mellitus (T2DM) who underwent BMS over a 6-year period (2016-2022) at a regional tertiary referral following completion of a specialist multidisciplinary weight management. Post-operative outcomes of interest included 30-day mortality, readmission rates, need for Intensive Care Unit (ICU) care and hospital length of stay (LOS) and were assessed according to HbA1c cut-off values of < 69 (N = 202) and > 69 mmol/mol (N = 67) as well as a continuous variable. RESULTS A total of 269 patients with T2D were included in this study. Patients underwent primary Roux en-Y gastric bypass (RYGB, n = 136), Sleeve Gastrectomy (SG, n = 124), insertion of gastric band (n = 4) or one-anastomosis gastric bypass (OAGB, n = 4). No significant differences in the rates of complications were observed between the two groups of pre-operative HbA1c cut-off values. No HbA1c threshold was observed for glycaemic control that would affect the peri- and post-operative complications following BMS. CONCLUSIONS We observed no associations between pre-operative HbA1C values and the risk of peri- and post-operative complications. In the context of a specialist multidisciplinary weight management programme, optimising pre-operative HbA1C to a recommended target value prior to BMS may not translate into reduced risks of peri- and post-operative complications.
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Affiliation(s)
- Rebekah Wilmington
- Clinical, Metabolic and Molecular Physiology Research Group, MRC-Versus Arthritis Centre for Musculoskeletal Ageing Research, University of Nottingham, Royal Derby Hospital Centre, Uttoxeter Road, Derby, DE22 3NE, UK.
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Nottingham, UK.
- East Midlands Bariatric & Metabolic Institute (EMBMI), Royal Derby Hospital, University Hospitals of Derby & Burton NHS Foundation Trust, Derby, UK.
| | - Mahmoud Abuawwad
- East Midlands Bariatric & Metabolic Institute (EMBMI), Royal Derby Hospital, University Hospitals of Derby & Burton NHS Foundation Trust, Derby, UK
| | - Guy Holt
- East Midlands Bariatric & Metabolic Institute (EMBMI), Royal Derby Hospital, University Hospitals of Derby & Burton NHS Foundation Trust, Derby, UK
| | - Robyn Anderson
- East Midlands Bariatric & Metabolic Institute (EMBMI), Royal Derby Hospital, University Hospitals of Derby & Burton NHS Foundation Trust, Derby, UK
| | - Rami Aldafas
- Clinical, Metabolic and Molecular Physiology Research Group, MRC-Versus Arthritis Centre for Musculoskeletal Ageing Research, University of Nottingham, Royal Derby Hospital Centre, Uttoxeter Road, Derby, DE22 3NE, UK
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Nottingham, UK
- Faculty of Public Health, College of Health Science, The Saudi Electronic University, Riyadh, Saudi Arabia
| | - Sherif Awad
- East Midlands Bariatric & Metabolic Institute (EMBMI), Royal Derby Hospital, University Hospitals of Derby & Burton NHS Foundation Trust, Derby, UK
| | - Iskandar Idris
- Clinical, Metabolic and Molecular Physiology Research Group, MRC-Versus Arthritis Centre for Musculoskeletal Ageing Research, University of Nottingham, Royal Derby Hospital Centre, Uttoxeter Road, Derby, DE22 3NE, UK
- National Institute for Health Research (NIHR) Nottingham Biomedical Research Centre, Nottingham, UK
- East Midlands Bariatric & Metabolic Institute (EMBMI), Royal Derby Hospital, University Hospitals of Derby & Burton NHS Foundation Trust, Derby, UK
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5
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Sun S, Huang W, Wang Z, Xie W, Zhou J, He Q. Association of Malnutrition Diagnosed Using Global Leadership Initiative on Malnutrition Criteria with Severe Postoperative Complications After Gastrectomy in Patients with Gastric Cancer. J Laparoendosc Adv Surg Tech A 2023; 33:1193-1200. [PMID: 37787912 DOI: 10.1089/lap.2023.0310] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/04/2023] Open
Abstract
Background: The purpose of this study was to investigate the relationship between malnutrition assessed by the Global Leadership Initiative on Malnutrition (GLIM) criteria and the occurrence of severe postoperative complications (SPCs) after gastrectomy in patients with gastric cancer. Methods: A total of 220 patients with gastric cancer were included in this retrospective study. According to the GLIM criteria, the first step was to use the Nutrition Risk Screening Score 2002 to conduct nutritional risk screening for patients and the second step was to diagnose and grade the severity of malnutrition in patients at risk of malnutrition. According to the Clavien-Dindo classification system, SPCs were defined as C-D Grade IIIa or higher. Results: Overall, 66 (30.0%) patients were diagnosed with malnutrition, including 32 (14.5%) with moderate malnutrition and 34 (15.5%) with severe malnutrition. The incidence of SPCs was 14.5%, and the most frequent postoperative event was anastomotic leakage. In the multivariate regression analysis, malnutrition was considered an independent risk factor for SPCs (P < .001). After adjusting for various factors, the grading association remained statistically significant. Compared with patients with normal nutrition, patients with moderate and severe malnutrition have a nearly 15-fold (OR = 15.682, 95% CI: 4.481-54.877, P < .001) and 20-fold (OR = 20.554, 95% CI: 5.771-73.202, P < .001) increased risk of developing SPCs, respectively. Conclusions: Malnutrition assessed by GLIM was an independent risk factor for SPCs in gastric cancer patients. Therefore, early identification of malnourished patients is crucial for timely implementation of nutritional treatment and reducing the occurrence of postoperative complications.
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Affiliation(s)
- Sida Sun
- Department of Gastrointestinal Surgery 1 Section, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Wenting Huang
- Department of Gastrointestinal Surgery 1 Section, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Ziyi Wang
- Department of Gastrointestinal Surgery 1 Section, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Wenhui Xie
- Department of Gastrointestinal Surgery 1 Section, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Junfeng Zhou
- Department of Gastrointestinal Surgery 1 Section, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
| | - Qingliang He
- Department of Gastrointestinal Surgery 1 Section, The First Affiliated Hospital of Fujian Medical University, Fuzhou, China
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Thongprayoon C, Tangpanithandee S, Jadlowiec CC, Mao SA, Mao MA, Vaitla P, Acharya PC, Leeaphorn N, Kaewput W, Pattharanitima P, Suppadungsuk S, Krisanapan P, Nissaisorakarn P, Cooper M, Craici IM, Cheungpasitporn W. Characteristics of Kidney Transplant Recipients with Prolonged Pre-Transplant Dialysis Duration as Identified by Machine Learning Consensus Clustering: Pathway to Personalized Care. J Pers Med 2023; 13:1273. [PMID: 37623523 PMCID: PMC10455164 DOI: 10.3390/jpm13081273] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2023] [Revised: 08/16/2023] [Accepted: 08/17/2023] [Indexed: 08/26/2023] Open
Abstract
Longer pre-transplant dialysis duration is known to be associated with worse post-transplant outcomes. Our study aimed to cluster kidney transplant recipients with prolonged dialysis duration before transplant using an unsupervised machine learning approach to better assess heterogeneity within this cohort. We performed consensus cluster analysis based on recipient-, donor-, and transplant-related characteristics in 5092 kidney transplant recipients who had been on dialysis ≥ 10 years prior to transplant in the OPTN/UNOS database from 2010 to 2019. We characterized each assigned cluster and compared the posttransplant outcomes. Overall, the majority of patients with ≥10 years of dialysis duration were black (52%) or Hispanic (25%), with only a small number (17.6%) being moderately sensitized. Within this cohort, three clinically distinct clusters were identified. Cluster 1 patients were younger, non-diabetic and non-sensitized, had a lower body mass index (BMI) and received a kidney transplant from younger donors. Cluster 2 recipients were older, unsensitized and had a higher BMI; they received kidney transplant from older donors. Cluster 3 recipients were more likely to be female with a higher PRA. Compared to cluster 1, cluster 2 had lower 5-year death-censored graft (HR 1.40; 95% CI 1.16-1.71) and patient survival (HR 2.98; 95% CI 2.43-3.68). Clusters 1 and 3 had comparable death-censored graft and patient survival. Unsupervised machine learning was used to characterize kidney transplant recipients with prolonged pre-transplant dialysis into three clinically distinct clusters with variable but good post-transplant outcomes. Despite a dialysis duration ≥ 10 years, excellent outcomes were observed in most recipients, including those with moderate sensitization. A disproportionate number of minority recipients were observed within this cohort, suggesting multifactorial delays in accessing kidney transplantation.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
| | - Supawit Tangpanithandee
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Caroline C. Jadlowiec
- Division of Nephrology, University of Mississippi Medical Center, Jackson, MS 39216, USA;
| | - Shennen A. Mao
- Division of Transplant Surgery, Mayo Clinic, Phoenix, AZ 85054, USA;
| | - Michael A. Mao
- Division of Transplant Surgery, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Pradeep Vaitla
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Jacksonville, FL 32224, USA;
| | - Prakrati C. Acharya
- Division of Nephrology, Texas Tech Health Sciences Center El Paso, El Paso, TX 79905, USA;
| | - Napat Leeaphorn
- Renal Transplant Program, University of Missouri-Kansas City School of Medicine/Saint Luke’s Health System, Kansas City, MO 64108, USA;
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand;
| | - Pattharawin Pattharanitima
- Division of Nephrology, Department of Internal Medicine, Faculty of Medicine Thammasat University, Pathum Thani 12120, Thailand;
| | - Supawadee Suppadungsuk
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
- Chakri Naruebodindra Medical Institute, Faculty of Medicine Ramathibodi Hospital, Mahidol University, Samut Prakan 10540, Thailand
| | - Pajaree Krisanapan
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
- Division of Nephrology, Department of Internal Medicine, Faculty of Medicine Thammasat University, Pathum Thani 12120, Thailand;
- Division of Nephrology, Department of Internal Medicine, Thammasat University Hospital, Pathum Thani 12120, Thailand
| | - Pitchaphon Nissaisorakarn
- Deparment of Medicine, Division of Nephrology, Massachusetts General Hospital, Harvard Medical School, Boston, MA 02114, USA;
| | - Matthew Cooper
- Department of Surgery, Medical College of Wisconsin, Milwaukee, WI 53226, USA;
| | - Iasmina M. Craici
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
| | - Wisit Cheungpasitporn
- Division of Nephrology and Hypertension, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA; (S.T.); (S.S.); (P.K.); (I.M.C.); (W.C.)
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Sun S, Stenberg E, Lindholm L, Salén KG, Franklin KA, Luo N, Cao Y. Prediction of quality-adjusted life years (QALYs) after bariatric surgery using regularized linear regression models: results from a Swedish nationwide quality register. Obes Surg 2023; 33:2452-2462. [PMID: 37322243 PMCID: PMC10345068 DOI: 10.1007/s11695-023-06685-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Revised: 05/25/2023] [Accepted: 06/08/2023] [Indexed: 06/17/2023]
Abstract
PURPOSE To investigate whether the quality-adjusted life years (QALYs) of the patients who underwent bariatric surgery could be predicted using their baseline information. MATERIALS AND METHODS All patients who received bariatric surgery in Sweden between January 1, 2011 and March 31, 2019 were obtained from the Scandinavian Obesity Surgery Registry (SOReg). Baseline information included patients' sociodemographic characteristics, details regarding the procedure, and postsurgical conditions. QALYs were assessed by the SF-6D at follow-up years 1 and 2. The general and regularized linear regression models were used to predict postoperative QALYs. RESULTS All regression models demonstrated satisfactory and comparable performance in predicting QALYs at follow-up year 1, with R2 and relative root mean squared error (RRMSE) values of about 0.57 and 9.6%, respectively. The performance of the general linear regression model increased with the number of variables; however, the improvement was ignorable when the number of variables was more than 30 and 50 for follow-up years 1 and 2, respectively. Although minor L1 and L2 regularization provided better prediction ability, the improvement was negligible when the number of variables was more than 20. All the models showed poorer performance for predicting QALYs at follow-up year 2. CONCLUSIONS Patient characteristics before bariatric surgery including health related quality of life, age, sex, BMI, postoperative complications within six weeks, and smoking status, may be adequate in predicting their postoperative QALYs after one year. Understanding these factors can help identify individuals who require more personalized and intensive support before, during, and after surgery.
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Affiliation(s)
- Sun Sun
- Department of Epidemiology and Global Health, Umeå University, 901 87, Umeå, Sweden.
| | - Erik Stenberg
- Department of Surgery, Faculty of Medicine and Health, Örebro University, 701 85, Örebro, Sweden
| | - Lars Lindholm
- Department of Epidemiology and Global Health, Umeå University, 901 87, Umeå, Sweden
| | - Klas-Göran Salén
- Department of Epidemiology and Global Health, Umeå University, 901 87, Umeå, Sweden
| | - Karl A Franklin
- Department of Surgical and Perioperative Sciences, Surgery, Umeå University, Umeå, Sweden
| | - Nan Luo
- Saw Swee Hock School of Public Health, National University of Singapore, Singapore, Singapore
| | - Yang Cao
- Clinical Epidemiology and Biostatistics, School of Medical Sciences, Faculty of Medicine and Health, Örebro University, 701 82, Örebro, Sweden.
- Unit of Integrative Epidemiology, Institute of Environmental Medicine, Karolinska Institutet, 171 77, Stockholm, Sweden.
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Yu X, Wu P, Wang Z, Han W, Huang Y, Xin S, Zhang Q, Zhao S, Sun H, Lei G, Zhang T, Zhang L, Shen Y, Gu W, Li H, Jiang J. Network prediction of surgical complication clusters: a prospective multicenter cohort study. SCIENCE CHINA. LIFE SCIENCES 2023; 66:1636-1646. [PMID: 36881319 DOI: 10.1007/s11427-022-2200-1] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 05/22/2022] [Accepted: 10/11/2022] [Indexed: 03/08/2023]
Abstract
Complicated relationships exist in both occurrence and progression of surgical complications, which are difficult to account for using a separate quantitative method such as prediction or grading. Data of 51,030 surgical inpatients were collected from four academic/teaching hospitals in a prospective cohort study in China. The relationship between preoperative factors, 22 common complications, and death was analyzed. With input from 54 senior clinicians and following a Bayesian network approach, a complication grading, cluster-visualization, and prediction (GCP) system was designed to model pathways between grades of complication and preoperative risk factor clusters. In the GCP system, there were 11 nodes representing six grades of complication and five preoperative risk factor clusters, and 32 arcs representing a direct association. Several critical targets were pinpointed on the pathway. Malnourished status was a fundamental cause widely associated (7/32 arcs) with other risk factor clusters and complications. American Society of Anesthesiologists (ASA) score ⩾3 was directly dependent on all other risk factor clusters and influenced all severe complications. Grade III complications (mainly pneumonia) were directly dependent on 4/5 risk factor clusters and affected all other grades of complication. Irrespective of grade, complication occurrence was more likely to increase the risk of other grades of complication than risk factor clusters.
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Affiliation(s)
- Xiaochu Yu
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China.
| | - Peng Wu
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Zixing Wang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Wei Han
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Yuguang Huang
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Shijie Xin
- The First Hospital of China Medical University, Shenyang, 110001, China
| | - Qiang Zhang
- Qinghai Provincial People's Hospital, Xining, 810007, China
| | - Shengxiu Zhao
- Qinghai Provincial People's Hospital, Xining, 810007, China
| | - Hong Sun
- Xiangya Hospital of Central South University, Changsha, 410008, China
| | - Guanghua Lei
- Xiangya Hospital of Central South University, Changsha, 410008, China
| | - Taiping Zhang
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences, Beijing, 100730, China
| | - Luwen Zhang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Yubing Shen
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Wentao Gu
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China
| | - Hongwei Li
- Research Department, PaodingAI, Beijing, 100083, China
| | - Jingmei Jiang
- Institute of Basic Medical Sciences, Chinese Academy of Medical Sciences / School of Basic Medicine, Peking Union Medical College, Beijing, 100005, China.
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9
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Caiazzo R, Marciniak C, Rémond A, Baud G, Pattou F. Future of bariatric surgery beyond simple weight loss: Metabolic surgery. J Visc Surg 2023; 160:S55-S62. [PMID: 36774271 DOI: 10.1016/j.jviscsurg.2023.01.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/11/2023]
Abstract
Anatomical modifications implemented during bariatric surgery not only result in weight loss, but also lead to metabolic corrections that translate into better glycemia stability and improvement in cardiovascular and liver disorders. The logical extension of surgical indications beyond mere reduction of the body mass index (BMI) (i.e. patients with<35kg/m2) is a hot topic today in France and worldwide. Metabolic surgeries make use of multiple modalities (endoscopic, mini-invasive, invasive) that should be carried out by trained physicians and within the same type of multidisciplinary formation as that for bariatric surgery. The aim of this update is to describe the physiological mechanisms that result in the benefits of bariatric surgery, the various procedures currently available and the perspectives for this new field in visceral and digestive surgery.
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Affiliation(s)
- R Caiazzo
- General and Endocrine Surgery Department, Inuversity Hospital of Lille, Lille, France.
| | - C Marciniak
- General and Endocrine Surgery Department, Inuversity Hospital of Lille, Lille, France
| | - A Rémond
- General and Endocrine Surgery Department, Inuversity Hospital of Lille, Lille, France
| | - G Baud
- General and Endocrine Surgery Department, Inuversity Hospital of Lille, Lille, France
| | - F Pattou
- General and Endocrine Surgery Department, Inuversity Hospital of Lille, Lille, France
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10
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Shah K, Gögenur I, Gislason H. High preoperative HbA1c does not affect early or late complication rates after bariatric surgery. Surg Endosc 2023:10.1007/s00464-023-10009-w. [PMID: 36991264 DOI: 10.1007/s00464-023-10009-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/11/2022] [Accepted: 03/09/2023] [Indexed: 03/30/2023]
Abstract
BACKGROUND Preoperative HbA1c has been associated with an increased incidence of postoperative morbidity and mortality after abdominal and cardiovascular surgery. The literature on bariatric surgery is inconclusive and guidelines recommend postponement of surgery when HbA1c is above an arbitrary threshold (≥ 8.5%). In this study, we sought to understand the impact of preoperative HbA1c on early and late postoperative complications. METHODS We performed a retrospective analysis of prospectively collected data on obese patients with diabetes who underwent laparoscopic bariatric surgery. Patients were categorized into three groups according to their preoperative HbA1c level: < 6.5% (group 1), 6.5-8.4% (group 2) and ≥ 8.5% (group 3). Primary outcomes were early and late postoperative complications (< and > 30 days, respectively) that were differentiated based on severity (major/minor). Secondary outcomes were length of stay (LOS), duration of surgery, and rate of readmission. RESULTS In total, 6798 patients underwent laparoscopic bariatric surgery from 2006 to 2016, of which 1021 (15%) patients had Type 2 Diabetes (T2D). Complete data with a median follow-up of 45 months (3-120) were available for 914 patients with HbA1c < 6.5% (n = 227, 24.9%), 6.5-8.4% (n = 532, 58.5%) and ≥ 8.5% (n = 152, 16.6%). Early major surgical complication rate was similar across the groups ranging from 2.6 to 3.3%. No associations between high preoperative HbA1c and late complications-medical as well as surgical-was observed. Groups 2 and 3 had statistically significant more pronounced inflammatory status. LOS (1.8-1.9 days), readmission rates (1.7-2.0%) and surgical time was similar across the three groups. CONCLUSION Elevated HbA1c is not associated with more early or late postoperative complications, longer LOS, longer surgical time or higher rates of readmission.
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11
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Yu X, Huang YH, Feng YZ, Cheng ZY, Wang CC, Cai XR. Association of body composition with postoperative complications after laparoscopic sleeve gastrectomy and Roux-en-Y gastric bypass. Eur J Radiol 2023; 162:110768. [PMID: 36913816 DOI: 10.1016/j.ejrad.2023.110768] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 03/02/2023] [Accepted: 03/06/2023] [Indexed: 03/11/2023]
Abstract
OBJECTIVE To evaluate predictive values of body composition parameters measured from preoperative CT/MRIs for postoperative complications after laparoscopic sleeve gastrectomy (LSG) and Roux-en-Y gastric bypass (LRYGB) in patients with obesity. METHODS In this retrospective case-control study, patients performing abdominal CT/MRIs within one month before and developing 30-day complications after bariatric procedures were matched for age, sex, and type of surgery with patients without complications (1/3 ratio, respectively). Complications were determined by documentation in the medical record. Two readers blindly segmented the total abdominal muscle area (TAMA) and visceral fat area (VFA) using predetermined thresholds for the Hounsfield unit (HU) on unenhanced CT and the signal intensity (SI) on T1-weighted MRI at the L3 vertebral level. Visceral obesity (VO) was defined as VFA > 136 cm2 in males and > 95 cm2 in females. These measures, along with perioperative variables, were compared. Multivariate logistic regression analyses were performed. RESULTS Of 145 included patients, 36 had postoperative complications. No significant differences between LSG and LRYGB were present regarding complications and VO. Hypertension (p = 0.022), impaired lung function (p = 0.018), American Society of Anesthesiologists (ASA) grade (p = 0.046), VO (p = 0.021), and VFA/TAMA ratio (p < 0.0001) were associated with postoperative complications in the univariate logistic analysis; the VFA/TAMA ratio was the only independent predictor in multivariate analyses (OR 2.01, 95% CI 1.37-2.93, p < 0.001). CONCLUSION The VFA/TAMA ratio provides important perioperative information in predicting patients who are likely to develop postoperative complications undergoing bariatric surgery.
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Affiliation(s)
- Xin Yu
- Medical Imaging Centre, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - Yan-Hao Huang
- Medical Imaging Centre, First Affiliated Hospital of Jinan University, Guangzhou, China
| | - You-Zhen Feng
- Medical Imaging Centre, First Affiliated Hospital of Jinan University, Guangzhou, China.
| | - Zhong-Yuan Cheng
- Medical Imaging Centre, First Affiliated Hospital of Jinan University, Guangzhou, China.
| | - Cun-Chuan Wang
- Department of General Surgery, First Affiliated Hospital of Jinan University, Guangzhou, China.
| | - Xiang-Ran Cai
- Medical Imaging Centre, First Affiliated Hospital of Jinan University, Guangzhou, China.
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12
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Al-Mazrou AM, Bellorin O, Dhar V, Dakin G, Afaneh C. Selection of Robotic Bariatric Surgery Candidates: a Nationwide Analysis. J Gastrointest Surg 2023; 27:903-913. [PMID: 36737593 DOI: 10.1007/s11605-023-05595-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 10/02/2022] [Accepted: 01/07/2023] [Indexed: 02/05/2023]
Abstract
INTRODUCTION This study aims to identify risk factors associated with 30-day major complications, readmission, and delayed discharge for patients undergoing robotic bariatric surgery. METHODS From the metabolic and bariatric surgery and accreditation quality improvement program (2015-2018) datasets, adult patients who underwent elective robotic bariatric operations were included. Predictors for 30-day major complications, readmission, and delayed discharge (hospital stay ≥ 3 days) were identified using univariable and multivariable analyses. RESULTS Major complications in patients undergoing robotic bariatric surgery were associated with both pre-operative and intraoperative factors including pre-existing cardiac morbidity (OR = 1.41, CI = [1.09-1.82]), gastroesophageal reflux disease [GERD] (OR = 1.23, CI = [1.11-1.38]), pulmonary embolism (OR = 1.51, CI = [1.02-2.22]), prior bariatric surgery (OR = 1.66, CI = [1.43-1.94]), increased operating time (OR = 1.003, CI = [1.002-1.004]), gastric bypass or duodenal switch (OR = 1.58, CI = [1.40-1.79]), and intraoperative drain placement (OR = 1.28, CI = [1.11-1.47]). With regard to 30-day readmission, non-white race (OR = 1.25, CI = [1.14-1.39]), preoperative hyperlipidemia (OR = 1.16, CI = [1.14-1.38]), DVT (OR = 1.48, CI = [1.10-1.99]), therapeutic anticoagulation (OR = 1.48, CI = [1.16-1.89]), limited ambulation (OR = 1.33, CI = [1.01-1.74]), and dialysis (OR = 2.14, CI = [1.13-4.09]) were significantly associated factors. Age ≥ 65 (OR = 1.18, CI = [1.04-1.34]), female gender (OR = 1.21, CI = [1.10-1.32]), hypertension (OR = 1.08, CI = [1.01-1.15]), renal insufficiency (OR = 2.32, CI = [1.69-3.17]), COPD (OR = 1.49, CI = [1.23-1.82]), sleep apnea (OR = 1.10, CI = [1.03-1.18]), oxygen dependence (OR = 1.47, CI = [1.10-2.0]), steroid use (OR = 1.26, CI = [1.02-1.55]), IVC filter (OR = 1.52, CI = [1.15-2.0]), and BMI ≥ 40 (OR = 1.12, CI = [1.04-1.21]) were risk factors associated with delayed discharge. CONCLUSION When selecting patients for bariatric surgery, surgeons early in their learning curve for utilizing robotics should avoid individuals with pre-existing cardiac or renal morbidities, venous thromboembolism, and limited functional status. Patients who have had previous bariatric surgery or require technically demanding operations are at higher risk for complications. An evidence-based approach in selecting bariatric candidates may potentially minimize the overall costs associated with adopting the technology.
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Affiliation(s)
- Ahmed M Al-Mazrou
- Division of GI Metabolic and Bariatric Surgery, Department of Surgery, NewYork-Presbyterian Hospital/Weill Cornell Medicine, 525 East 68Th Street Box 294, New York, NY, 10065, USA
| | - Omar Bellorin
- Division of GI Metabolic and Bariatric Surgery, Department of Surgery, NewYork-Presbyterian Hospital/Weill Cornell Medicine, 525 East 68Th Street Box 294, New York, NY, 10065, USA
| | - Vikrom Dhar
- Division of GI Metabolic and Bariatric Surgery, Department of Surgery, NewYork-Presbyterian Hospital/Weill Cornell Medicine, 525 East 68Th Street Box 294, New York, NY, 10065, USA
| | - Gregory Dakin
- Division of GI Metabolic and Bariatric Surgery, Department of Surgery, NewYork-Presbyterian Hospital/Weill Cornell Medicine, 525 East 68Th Street Box 294, New York, NY, 10065, USA
| | - Cheguevara Afaneh
- Division of GI Metabolic and Bariatric Surgery, Department of Surgery, NewYork-Presbyterian Hospital/Weill Cornell Medicine, 525 East 68Th Street Box 294, New York, NY, 10065, USA.
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13
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Enodien B, Taha-Mehlitz S, Saad B, Nasser M, Frey DM, Taha A. The development of machine learning in bariatric surgery. Front Surg 2023; 10:1102711. [PMID: 36911599 PMCID: PMC9998495 DOI: 10.3389/fsurg.2023.1102711] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/19/2022] [Accepted: 02/08/2023] [Indexed: 03/14/2023] Open
Abstract
Background Machine learning (ML), is an approach to data analysis that makes the process of analytical model building automatic. The significance of ML stems from its potential to evaluate big data and achieve quicker and more accurate outcomes. ML has recently witnessed increased adoption in the medical domain. Bariatric surgery, otherwise referred to as weight loss surgery, reflects the series of procedures performed on people demonstrating obesity. This systematic scoping review aims to explore the development of ML in bariatric surgery. Methods The study used the Preferred Reporting Items for Systematic and Meta-analyses for Scoping Review (PRISMA-ScR). A comprehensive literature search was performed of several databases including PubMed, Cochrane, and IEEE, and search engines namely Google Scholar. Eligible studies included journals published from 2016 to the current date. The PRESS checklist was used to evaluate the consistency demonstrated during the process. Results A total of seventeen articles qualified for inclusion in the study. Out of the included studies, sixteen concentrated on the role of ML algorithms in prediction, while one addressed ML's diagnostic capacity. Most articles (n = 15) were journal publications, whereas the rest (n = 2) were papers from conference proceedings. Most included reports were from the United States (n = 6). Most studies addressed neural networks, with convolutional neural networks as the most prevalent. Also, the data type used in most articles (n = 13) was derived from hospital databases, with very few articles (n = 4) collecting original data via observation. Conclusions This study indicates that ML has numerous benefits in bariatric surgery, however its current application is limited. The evidence suggests that bariatric surgeons can benefit from ML algorithms since they will facilitate the prediction and evaluation of patient outcomes. Also, ML approaches to enhance work processes by making data categorization and analysis easier. However, further large multicenter studies are required to validate results internally and externally as well as explore and address limitations of ML application in bariatric surgery.
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Affiliation(s)
- Bassey Enodien
- Department of Surgery, GZO-Hospital, Wetzikon, Switzerland
| | - Stephanie Taha-Mehlitz
- Clarunis, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, Basel, Switzerland
| | - Baraa Saad
- School of Medicine, St George's University of London, London, United Kingdom
| | - Maya Nasser
- School of Medicine, St George's University of London, London, United Kingdom
| | - Daniel M Frey
- Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
| | - Anas Taha
- Clarunis, University Centre for Gastrointestinal and Liver Diseases, St. Clara Hospital and University Hospital, Basel, Switzerland.,Department of Biomedical Engineering, Faculty of Medicine, University of Basel, Allschwil, Switzerland
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14
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Skogar ML, Stenberg E, Sundbom M. No Weekday Effect in Bariatric Surgery-a Retrospective Cohort Study. Obes Surg 2022; 32:1990-1995. [PMID: 35378660 PMCID: PMC9072493 DOI: 10.1007/s11695-022-06041-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/21/2022] [Revised: 03/21/2022] [Accepted: 03/22/2022] [Indexed: 11/25/2022]
Abstract
PURPOSE Major abdominal surgery carried out in the later part of the week has been associated with increased complication rates. The aim of this study was to explore whether the weekday of surgery affects the 30-day complication risks after primary Roux-en-Y gastric bypass (RYGB) and sleeve gastrectomy (SG). MATERIAL AND METHODS Prospectively collected data, extracted from the Scandinavian Obesity Surgery Registry (SOReg), of all patients who underwent primary laparoscopic RYGB or SG between 2010 and 2017 were included in this retrospective cohort study. Multivariate logistic regression adjusted for differences in case-mix and operating center by weekday of surgery. RESULTS In total, 49,349 patients were included in this study. The overall 30-day complication rate was 7.2% (n = 3574), whereof 2.9% (n = 1428) had a severe complication, i.e., requiring intervention in general anesthesia or more. The 30-day mortality rate and readmission rate were 0.02% (n = 12) and 7.6% (n = 3726), respectively. The highest overall complication rate was seen in patients operated on Wednesdays and Thursdays (7.7%), while severe complications were most common on Wednesdays (3.3%). However, a large variation in severe complications was seen between centers, from 0.4 to 8.0%. After adjustment for case-mix and operating center, there was no significant increased risk of overall complications, severe complications, or readmission rates by weekday of surgery, except for a lower readmission rate in patients operated on Tuesdays. CONCLUSION The result of the present study supports the notion that bariatric surgery can be performed safely on all weekdays.
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Affiliation(s)
- Martin L Skogar
- Department of Surgical Sciences, Uppsala University, 751 85, Uppsala, Sweden.
| | - Erik Stenberg
- Department of Surgery, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Magnus Sundbom
- Department of Surgical Sciences, Uppsala University, 751 85, Uppsala, Sweden
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15
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Karpińska I, Kulawik J, Małczak P, Wierdak M, Pędziwiatr M, Major P. Predicting complications following bariatric surgery: the diagnostic accuracy of available tools. Surg Obes Relat Dis 2022; 18:872-886. [DOI: 10.1016/j.soard.2022.03.021] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2021] [Revised: 03/05/2022] [Accepted: 03/18/2022] [Indexed: 12/19/2022]
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16
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Role of Platelet to Lymphocyte Ratio and Red Cell Distribution Width in Predicting Postoperative Complications in Patients with Acute Mesenteric Ischemia. Ann Vasc Surg 2022; 84:298-304. [PMID: 35247535 DOI: 10.1016/j.avsg.2022.01.036] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2020] [Revised: 12/20/2021] [Accepted: 01/26/2022] [Indexed: 12/23/2022]
Abstract
BACKGROUND The predictive values of the platelet to lymphocyte ratio (PLR) and red cell distribution width (RDW) have been demonstrated in different types of abdominal surgery. The aim of this study was to investigate the interest of the preoperative PLR and RDW as predictors of 30-day postoperative complications in patients with acute mesenteric ischemia (AMI). METHODS Clinical data of 105 AMI patients were retrospectively reviewed. Postoperative complications were evaluated by the Clavien-Dindo classification. The cutoff values for neutrophil to lymphocyte ratio (NLR), PLR, and RDW were determined by receiver operating characteristic curves. Univariate and multivariate analyses evaluating the risk factors for postoperative complications were performed. RESULTS In the univariate analyses, advanced age, female, anemia, high white blood cell (WBC), high PLR, high NLR, high RDW, Charlson comorbidity index (CCI) score ≥2, and bowel resection were associated with the postoperative complications. A multivariable analysis revealed that advanced age, high PLR, high RDW, and bowel resection were independent predictors of postoperative complications. CONCLUSIONS The PLR and RDW might play important roles in evaluation of the risk of postoperative complications in AMI patients. The preoperative PLR and RDW are simple and useful predictors of postoperative complications in AMI patients.
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17
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Fan S, Zhao Z, Zhang Y, Yu H, Zheng C, Huang X, Yang Z, Xing M, Lu Q, Luo Y. Probability calibration-based prediction of recurrence rate in patients with diffuse large B-cell lymphoma. BioData Min 2021; 14:38. [PMID: 34389029 PMCID: PMC8362168 DOI: 10.1186/s13040-021-00272-9] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/27/2021] [Accepted: 08/08/2021] [Indexed: 12/21/2022] Open
Abstract
Background Although many patients receive good prognoses with standard therapy, 30–50% of diffuse large B-cell lymphoma (DLBCL) cases may relapse after treatment. Statistical or computational intelligent models are powerful tools for assessing prognoses; however, many cannot generate accurate risk (probability) estimates. Thus, probability calibration-based versions of traditional machine learning algorithms are developed in this paper to predict the risk of relapse in patients with DLBCL. Methods Five machine learning algorithms were assessed, namely, naïve Bayes (NB), logistic regression (LR), random forest (RF), support vector machine (SVM) and feedforward neural network (FFNN), and three methods were used to develop probability calibration-based versions of each of the above algorithms, namely, Platt scaling (Platt), isotonic regression (IsoReg) and shape-restricted polynomial regression (RPR). Performance comparisons were based on the average results of the stratified hold-out test, which was repeated 500 times. We used the AUC to evaluate the discrimination ability (i.e., classification ability) of the model and assessed the model calibration (i.e., risk prediction accuracy) using the H-L goodness-of-fit test, ECE, MCE and BS. Results Sex, stage, IPI, KPS, GCB, CD10 and rituximab were significant factors predicting the 3-year recurrence rate of patients with DLBCL. For the 5 uncalibrated algorithms, the LR (ECE = 8.517, MCE = 20.100, BS = 0.188) and FFNN (ECE = 8.238, MCE = 20.150, BS = 0.184) models were well-calibrated. The errors of the initial risk estimate of the NB (ECE = 15.711, MCE = 34.350, BS = 0.212), RF (ECE = 12.740, MCE = 27.200, BS = 0.201) and SVM (ECE = 9.872, MCE = 23.800, BS = 0.194) models were large. With probability calibration, the biased NB, RF and SVM models were well-corrected. The calibration errors of the LR and FFNN models were not further improved regardless of the probability calibration method. Among the 3 calibration methods, RPR achieved the best calibration for both the RF and SVM models. The power of IsoReg was not obvious for the NB, RF or SVM models. Conclusions Although these algorithms all have good classification ability, several cannot generate accurate risk estimates. Probability calibration is an effective method of improving the accuracy of these poorly calibrated algorithms. Our risk model of DLBCL demonstrates good discrimination and calibration ability and has the potential to help clinicians make optimal therapeutic decisions to achieve precision medicine.
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Affiliation(s)
- Shuanglong Fan
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.,Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China
| | - Zhiqiang Zhao
- Department of Hematology, Shanxi Cancer Hospital, Taiyuan, China
| | - Yanbo Zhang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.,Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China
| | - Hongmei Yu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.,Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China
| | - Chuchu Zheng
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.,Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China
| | - Xueqian Huang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.,Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China
| | - Zhenhuan Yang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.,Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China
| | - Meng Xing
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.,Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China
| | - Qing Lu
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, USA.
| | - Yanhong Luo
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China. .,Shanxi Provincial Key Laboratory of Major Diseases Risk Assessment, Taiyuan, China.
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18
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Torquati M, Mendis M, Xu H, Myneni AA, Noyes K, Hoffman AB, Omotosho P, Becerra AZ. Using the Super Learner algorithm to predict risk of 30-day readmission after bariatric surgery in the United States. Surgery 2021; 171:621-627. [PMID: 34340821 DOI: 10.1016/j.surg.2021.06.019] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/16/2021] [Revised: 06/04/2021] [Accepted: 06/14/2021] [Indexed: 12/11/2022]
Abstract
BACKGROUND Risk prediction models that estimate patient probabilities of adverse events are commonly deployed in bariatric surgery. The objective was to validate a machine learning (Super Learner) prediction model of 30-day readmission after bariatric surgery in comparison with a traditional logistic regression. METHODS This prognostic study for validation of risk prediction models used data from the Metabolic and Bariatric Surgery Accreditation and Quality Improvement Program. Patients who underwent elective laparoscopic gastric bypass or laparoscopic sleeve gastrectomy between 2015 and 2018 were included. Models used 5-fold cross-validation and were evaluated using the area under the receiver operating characteristic curve, the net reclassification index, and the integrated discrimination improvement. RESULTS The 30-day readmission rate among 393,833 patients was 3.9%. Super Learner area under the receiver operating characteristic curve was 0.674 (95% confidence interval 0.670-0.679), compared to 0.650 (95% confidence interval 0.645-0.654) for logistic regression. The net reclassification index was 0.239 (95% confidence interval 0.223-0.254), and 0.252 (95% confidence interval 0.249-0.255) for those who were and were not readmitted within 30 days. The integrated discrimination improvement was 0.0032 (95% confidence interval 0.0030-0.0033). CONCLUSION The Super Learner outperformed traditional logistic regression in predicting risk of 30-day readmission after bariatric surgery. Machine learning models may help target high-risk patients more optimally and prevent unnecessary readmissions.
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Affiliation(s)
- Matteo Torquati
- Boston College, Morrissey College of Arts & Sciences, Boston, MA
| | | | - Huiwen Xu
- Department of Surgery, University of Rochester Medical Center, Rochester, NY. https://twitter.com/Dr_HuiwenXu
| | - Ajay A Myneni
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, NY
| | - Katia Noyes
- Department of Epidemiology and Environmental Health, School of Public Health and Health Professions, University at Buffalo, NY. https://twitter.com/KatiaPhd
| | - Aaron B Hoffman
- Department of Surgery, Jacobs School of Medicine and Biomedical Sciences, University at Buffalo, NY
| | - Philip Omotosho
- Department of Surgery, Rush University Medical Center, Chicago, IL
| | - Adan Z Becerra
- Department of Surgery, Rush University Medical Center, Chicago, IL.
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Meunier H, Menahem B, Le Roux Y, Bion AL, Marion Y, Vallois A, Contival N, Gautier T, Lubrano J, Briant A, Parienti JJ, Alves A. Development of the "OS-SEV90 Score" to Predict Severe Postoperative Complications at 90 Days Following Bariatric Surgery. Obes Surg 2021; 31:3053-3064. [PMID: 33907969 DOI: 10.1007/s11695-021-05367-0] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/14/2020] [Revised: 03/15/2021] [Accepted: 03/22/2021] [Indexed: 12/14/2022]
Abstract
BACKGROUND Bariatric surgery may be associated with severe postoperative complications (SPC). Factors associated with the risk of SPC have not been fully investigated. OBJECTIVES This study aimed to identify preoperative risk factors of SPC within 90 days and to develop a risk prediction model based on these factors. METHODS We conducted a retrospective single-center cohort study based on a prospectively maintained database of obese patients undergoing laparoscopic bariatric surgery from October 2005 to May 2019. All SPC occurring up to the 90th postoperative day were recorded according to the Dindo-Clavien classification. Associations between potential risk factors and SPC were analyzed using a logistic regression model, and the risk prediction ("OS-SEV90 score") was computed. Based on the OS-SEV90 score, the patients were grouped into 3 categories of risk: low, intermediate, and high. RESULTS Among 1963 consecutive patients, no patient died and 82 (4.2%) experienced SPC within 90 days. History of gastric or esophageal surgery (adjusted odds ratio (aOR) 3.040, 95% confidence interval; CI 1.78-5.20, p< 0.0001), past of thromboembolic event aOR 2.26, 95%; CI 1.12-4.55, p = 0.0225), and surgery performed by a junior surgeon (aOR 1.99, 95%; CI 1.26-3.13, p = 0.003) were all independently associated with risk for SPC, adjusting for ASA physical status system (ASA) score ≥ 3, severe OSA, psychiatric disease, asthma, a history of abdominal surgery, alcohol, cardiac disease, and dyslipidemia. "the OS-SEV90 score" based on these factors was constructed to classify patients into 3 risk groups: low (≤2), intermediate (3-4), and high (≥5). According to "the OS-SEV90 score," SPC increased significantly from 2.9% in the low-risk group, 7.7% in the intermediate-risk group, and 23.3% in the high-risk group. CONCLUSIONS A predictive model of SPC within 90 days "the OS-SEV90 score" has been developed using 9 baseline risk factors. The use of the OS-SEV90 score may help the multidisciplinary team to identify the specific risk of each patient and inform them about and optimize the comorbidities before the surgery. Further studies are warranted to validate this score in a new independent cohort before using it in clinical practice.
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Affiliation(s)
- Hugo Meunier
- Department of Digestive Surgery, University Hospital of Caen, Avenue de la côte de Nacre, 14033, Caen cedex, France
| | - Benjamin Menahem
- Department of Digestive Surgery, University Hospital of Caen, Avenue de la côte de Nacre, 14033, Caen cedex, France.
- UMR INSERM 1086 "Cancers et préventions", Centre François Baclesse, 3 avenue du Général Harris, 14045, Caen cedex, France.
- UFR de Médecine, 2 avenue des Rochambelles, CS 60001, 14033, Caen cedex, France.
| | - Yannick Le Roux
- Department of Digestive Surgery, University Hospital of Caen, Avenue de la côte de Nacre, 14033, Caen cedex, France
| | - Adrien Lee Bion
- Department of Digestive Surgery, University Hospital of Caen, Avenue de la côte de Nacre, 14033, Caen cedex, France
| | - Yoann Marion
- Department of Digestive Surgery, University Hospital of Caen, Avenue de la côte de Nacre, 14033, Caen cedex, France
| | - Antoine Vallois
- Department of Digestive Surgery, University Hospital of Caen, Avenue de la côte de Nacre, 14033, Caen cedex, France
| | - Nicolas Contival
- Department of Digestive Surgery, University Hospital of Caen, Avenue de la côte de Nacre, 14033, Caen cedex, France
| | - Thomas Gautier
- Department of Digestive Surgery, University Hospital of Caen, Avenue de la côte de Nacre, 14033, Caen cedex, France
| | - Jean Lubrano
- Department of Digestive Surgery, University Hospital of Caen, Avenue de la côte de Nacre, 14033, Caen cedex, France
- UFR de Médecine, 2 avenue des Rochambelles, CS 60001, 14033, Caen cedex, France
| | - Anaïs Briant
- Department of Biostatistics, University Hospital of Caen, Caen, France
| | - Jean-Jacques Parienti
- UFR de Médecine, 2 avenue des Rochambelles, CS 60001, 14033, Caen cedex, France
- Department of Biostatistics, University Hospital of Caen, Caen, France
| | - Arnaud Alves
- Department of Digestive Surgery, University Hospital of Caen, Avenue de la côte de Nacre, 14033, Caen cedex, France
- UMR INSERM 1086 "Cancers et préventions", Centre François Baclesse, 3 avenue du Général Harris, 14045, Caen cedex, France
- UFR de Médecine, 2 avenue des Rochambelles, CS 60001, 14033, Caen cedex, France
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Preoperative chronic opioid use and its impact on early complications in bariatric surgery: a Swedish nationwide cohort study of 56,183 patients. Surg Obes Relat Dis 2021; 17:1256-1262. [PMID: 33962877 DOI: 10.1016/j.soard.2021.04.008] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Revised: 03/18/2021] [Accepted: 04/04/2021] [Indexed: 12/29/2022]
Abstract
BACKGROUND The association between severe obesity and chronic pain makes opioid use common among bariatric patients. Preoperative opioid use has been identified as a risk factor in other surgical procedures. OBJECTIVES To examine the impact of preoperative opioid use on complications after primary bariatric surgery. SETTING Sweden. METHODS All primary laparoscopic Roux-en-Y gastric bypass (LRYGB) and laparoscopic sleeve gastrectomy (LSG) patients from 2007-2017 were identified in the Scandinavian Obesity Surgery Register. Prescriptions for opioids within 90 days prior to surgery were retrieved from the Swedish Prescribed Drug Register and converted into oral morphine equivalents (OMEs). Patients with ≥2 prescription of opioids within 90 days prior to surgery were defined as chronic opioid users. Generalized linear regression was used to adjust for age, sex, body mass index, procedure type, year of operation, and co-morbidities. RESULTS Of the 56,183 patients who had undergone primary LRYGB (n = 49,615) or LSG (n = 6568), 17.5% (n = 9825) had at least 1 prescription of opioids prior to surgery, of which 4.3% (n = 2390) were defined as chronic opioid users. Chronic opioid use was associated with a higher risk of severe complications (Clavien Dindo grade ≥ 3b; odds ratio [OR], 1.67; 95% confidence interval [CI], 1.37-2.04), increased lengths of stay (relative risk, 1.11; 95% CI, 1.08-1.14), and higher rates of readmission (OR, 1.70; 95% CI, 1.49-1.94) and reoperation (OR, 1.87; 95% CI, 1.53-2.27; all P values < .001). Furthermore, higher OME exposure was associated with stepwise higher risks. CONCLUSION Preoperative opioid use was an independent risk factor for severe complications, as well as prolonged lengths of stay, readmission, and reoperation after primary bariatric surgery.
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Fan S, Zhao Z, Yu H, Wang L, Zheng C, Huang X, Yang Z, Xing M, Lu Q, Luo Y. Applying probability calibration to ensemble methods to predict 2-year mortality in patients with DLBCL. BMC Med Inform Decis Mak 2021; 21:14. [PMID: 33413321 PMCID: PMC7791789 DOI: 10.1186/s12911-020-01354-0] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2020] [Accepted: 11/26/2020] [Indexed: 01/17/2023] Open
Abstract
BACKGROUND Under the influences of chemotherapy regimens, clinical staging, immunologic expressions and other factors, the survival rates of patients with diffuse large B-cell lymphoma (DLBCL) are different. The accurate prediction of mortality hazards is key to precision medicine, which can help clinicians make optimal therapeutic decisions to extend the survival times of individual patients with DLBCL. Thus, we have developed a predictive model to predict the mortality hazard of DLBCL patients within 2 years of treatment. METHODS We evaluated 406 patients with DLBCL and collected 17 variables from each patient. The predictive variables were selected by the Cox model, the logistic model and the random forest algorithm. Five classifiers were chosen as the base models for ensemble learning: the naïve Bayes, logistic regression, random forest, support vector machine and feedforward neural network models. We first calibrated the biased outputs from the five base models by using probability calibration methods (including shape-restricted polynomial regression, Platt scaling and isotonic regression). Then, we aggregated the outputs from the various base models to predict the 2-year mortality of DLBCL patients by using three strategies (stacking, simple averaging and weighted averaging). Finally, we assessed model performance over 300 hold-out tests. RESULTS Gender, stage, IPI, KPS and rituximab were significant factors for predicting the deaths of DLBCL patients within 2 years of treatment. The stacking model that first calibrated the base model by shape-restricted polynomial regression performed best (AUC = 0.820, ECE = 8.983, MCE = 21.265) in all methods. In contrast, the performance of the stacking model without undergoing probability calibration is inferior (AUC = 0.806, ECE = 9.866, MCE = 24.850). In the simple averaging model and weighted averaging model, the prediction error of the ensemble model also decreased with probability calibration. CONCLUSIONS Among all the methods compared, the proposed model has the lowest prediction error when predicting the 2-year mortality of DLBCL patients. These promising results may indicate that our modeling strategy of applying probability calibration to ensemble learning is successful.
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Affiliation(s)
- Shuanglong Fan
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Zhiqiang Zhao
- Department of Hematology, Shanxi Cancer Hospital, Taiyuan, China
| | - Hongmei Yu
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Lei Wang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Chuchu Zheng
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Xueqian Huang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Zhenhuan Yang
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Meng Xing
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China
| | - Qing Lu
- Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, USA.
| | - Yanhong Luo
- Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, China.
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Blume CA, Brust-Renck PG, Rocha MK, Leivas G, Neyeloff JL, Anzanello MJ, Fogliatto FS, Bahia LR, Telo GH, Schaan BD. Development and Validation of a Predictive Model of Success in Bariatric Surgery. Obes Surg 2020; 31:1030-1037. [PMID: 33190175 PMCID: PMC7666615 DOI: 10.1007/s11695-020-05103-0] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2020] [Revised: 10/28/2020] [Accepted: 10/29/2020] [Indexed: 11/26/2022]
Abstract
PURPOSE There are no criteria to establish priority for bariatric surgery candidates in the public health system in several countries. The aim of this study is to identify preoperative characteristics that allow predicting the success after bariatric surgery. MATERIALS AND METHODS Four hundred and sixty-one patients submitted to Roux-en-Y gastric bypass were included. Success of the surgery was defined as the sum of five outcome variables, assessed at baseline and 12 months after the surgery: excess weight loss, use of continuous positive airway pressure (CPAP) or bilevel positive airway pressure (BiPAP) as a treatment for obstructive sleep apnea (OSA), daily number of antidiabetics, daily number of antihypertensive drugs, and all-cause mortality. Partial least squares (PLS) regression and multiple linear regression were performed to identify preoperative predictors. We performed a 90/10 split of the dataset in train and test sets and ran a leave-one-out cross-validation on the train set and the best PLS model was chosen based on goodness-of-fit criteria. RESULTS The preoperative predictors of success after bariatric surgery included lower age, presence of non-alcoholic fatty liver disease and OSA, more years of CPAP/BiPAP use, negative history of cardiovascular disease, and lower number of antihypertensive drugs. The PLS model displayed a mean absolute percent error of 0.1121 in the test portion of the dataset, leading to accurate predictions of postoperative outcomes. CONCLUSION This success index allows prioritizing patients with the best indication for the procedure and could be incorporated in the public health system as a support tool in the decision-making process.
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Affiliation(s)
- Carina A. Blume
- Post-Graduate Program in Medical Sciences: Endocrinology, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS Brazil
| | - Priscila G. Brust-Renck
- Graduate School of Psychology, Universidade do Vale do Rio dos Sinos, São Leopoldo, RS Brazil
- Industrial & Transportation Eng. Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS Brazil
| | - Miriam K. Rocha
- Industrial & Transportation Eng. Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS Brazil
- Center of Engineering, Universidade Federal Rural do Semi-Árido, Mossoró, RN Brazil
| | - Gabriel Leivas
- School of Medicine, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS Brazil
| | - Jeruza L. Neyeloff
- National Institute of Science and Technology for Health Technology Assessment (IATS), Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Michel J. Anzanello
- Industrial & Transportation Eng. Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS Brazil
| | - Flavio S. Fogliatto
- Industrial & Transportation Eng. Department, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS Brazil
| | - Luciana R. Bahia
- National Institute of Science and Technology for Health Technology Assessment (IATS), Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
| | - Gabriela H. Telo
- School of Medicine/Graduate Program in Medicine and Health Sciences, Pontifícia Universidade Católica do Rio Grande do Sul, Porto Alegre, RS Brazil
| | - Beatriz D. Schaan
- Post-Graduate Program in Medical Sciences: Endocrinology, Universidade Federal do Rio Grande do Sul, Porto Alegre, RS Brazil
- National Institute of Science and Technology for Health Technology Assessment (IATS), Hospital de Clínicas de Porto Alegre, Porto Alegre, Brazil
- Endocrine Division, Hospital de Clínicas de Porto Alegre, Porto Alegre, RS Brazil
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Stenberg E, Mohseni S, Cao Y, Näslund E. Limited Effect of Beta-blockade on Postoperative Outcome After Laparoscopic Gastric Bypass Surgery. Obes Surg 2020; 30:139-145. [PMID: 31346982 DOI: 10.1007/s11695-019-04108-8] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND The benefit of beta-blockade on postoperative outcome remains controversial, though recent studies have suggested a role during major non-cardiac surgery. The benefit of beta-blockade during minimally invasive gastric bypass surgery remains unclear. The aim of the present study was to evaluate the possible association between preoperative beta-blocker therapy and postoperative outcome after laparoscopic gastric bypass surgery. METHODS Patients operated with primary laparoscopic gastric bypass surgery in Sweden between 2007 and 2017 were identified through the Scandinavian Obesity Surgery Registry. The dataset was linked to the Swedish National Patient Registry, the Swedish Prescribed Drug Registry, and Statistics Sweden. The main outcome was serious postoperative complication within 30 days of surgery; with postoperative complication, 90-day and 1-year mortality, and weight loss at 2 years after surgery as secondary endpoints. The Poisson regression model was used to evaluate primary and secondary categorical outcomes. A general mixed model was performed to evaluate 2-year weight loss. RESULTS In all, 50281 patients were included in the study. No difference was seen between patients on beta-blockade and the control group regarding postoperative complications (adjusted incidence rate ratio 1.04 (95%CI 0.93-1.15), p = 0.506), serious postoperative complication (adjusted IRR 1.06 95%CI 0.89-1.27), p = 0.515), 90-day mortality (adjusted IRR 0.71 (95%CI 0.24-2.10), p = 0.537), and 1-year mortality (adjusted IRR 1.26 (95%CI 0.67-2.36), p = 0.467). Weight loss 2 years after surgery was slightly greater in patients on beta-blockade (adjusted coefficient 0.53 (95%CI 0.19-0.87), p = 0.002). CONCLUSIONS Beta-blockade has limited impact on postoperative outcome after laparoscopic gastric bypass surgery.
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Affiliation(s)
- Erik Stenberg
- Department of Surgery, Faculty of Medicine and Health, Örebro University, SE-70185, Örebro, Sweden.
| | - Shahin Mohseni
- Department of Surgery, Faculty of Medicine and Health, Örebro University, SE-70185, Örebro, Sweden
| | - Yang Cao
- Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Erik Näslund
- Division of Surgery, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institute, Stockholm, Sweden
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Cao Y, Raoof M, Szabo E, Ottosson J, Näslund I. Using Bayesian Networks to Predict Long-Term Health-Related Quality of Life and Comorbidity after Bariatric Surgery: A Study Based on the Scandinavian Obesity Surgery Registry. J Clin Med 2020; 9:E1895. [PMID: 32560424 PMCID: PMC7356516 DOI: 10.3390/jcm9061895] [Citation(s) in RCA: 6] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Revised: 06/15/2020] [Accepted: 06/15/2020] [Indexed: 11/16/2022] Open
Abstract
Previously published literature has identified a few predictors of health-related quality of life (HRQoL) after bariatric surgery. However, performance of the predictive models was not evaluated rigorously using real world data. To find better methods for predicting prognosis in patients after bariatric surgery, we examined performance of the Bayesian networks (BN) method in predicting long-term postoperative HRQoL and compared it with the convolution neural network (CNN) and multivariable logistic regression (MLR). The patients registered in the Scandinavian Obesity Surgery Registry (SOReg) were used for the current study. In total, 6542 patients registered in the SOReg between 2008 and 2012 with complete demographic and preoperative comorbidity information, and preoperative and postoperative 5-year HROoL scores and comorbidities were included in the study. HRQoL was measured using the RAND-SF-36 and the obesity-related problems scale. Thirty-five variables were used for analyses, including 19 predictors and 16 outcome variables. The Gaussian BN (GBN), CNN, and a traditional linear regression model were used for predicting 5-year HRQoL scores, and multinomial discrete BN (DBN) and MLR were used for 5-year comorbidities. Eighty percent of the patients were randomly selected as a training dataset and 20% as a validation dataset. The GBN presented a better performance than the CNN and the linear regression model; it had smaller mean squared errors (MSEs) than those from the CNN and the linear regression model. The MSE of the summary physical scale was only 0.0196 for GBN compared to the 0.0333 seen in the CNN. The DBN showed excellent predictive ability for 5-year type 2 diabetes and dyslipidemia (area under curve (AUC) = 0.942 and 0.917, respectively), good ability for 5-year hypertension and sleep apnea syndrome (AUC = 0.891 and 0.834, respectively), and fair ability for 5-year depression (AUC = 0.750). Bayesian networks provide useful tools for predicting long-term HRQoL and comorbidities in patients after bariatric surgery. The hybrid network that may involve variables from different probability distribution families deserves investigation in the future.
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Affiliation(s)
- Yang Cao
- Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, 70182 Örebro, Sweden
| | - Mustafa Raoof
- Department of Surgery, Faculty of Medicine and Health, Örebro University, 70182 Örebro, Sweden; (M.R.); (E.S.); (J.O.); (I.N.)
| | - Eva Szabo
- Department of Surgery, Faculty of Medicine and Health, Örebro University, 70182 Örebro, Sweden; (M.R.); (E.S.); (J.O.); (I.N.)
| | - Johan Ottosson
- Department of Surgery, Faculty of Medicine and Health, Örebro University, 70182 Örebro, Sweden; (M.R.); (E.S.); (J.O.); (I.N.)
| | - Ingmar Näslund
- Department of Surgery, Faculty of Medicine and Health, Örebro University, 70182 Örebro, Sweden; (M.R.); (E.S.); (J.O.); (I.N.)
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Thongprayoon C, Hansrivijit P, Bathini T, Vallabhajosyula S, Mekraksakit P, Kaewput W, Cheungpasitporn W. Predicting Acute Kidney Injury after Cardiac Surgery by Machine Learning Approaches. J Clin Med 2020; 9:jcm9061767. [PMID: 32517295 PMCID: PMC7355827 DOI: 10.3390/jcm9061767] [Citation(s) in RCA: 9] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 06/04/2020] [Indexed: 02/08/2023] Open
Abstract
Cardiac surgery-associated AKI (CSA-AKI) is common after cardiac surgery and has an adverse impact on short- and long-term mortality. Early identification of patients at high risk of CSA-AKI by applying risk prediction models allows clinicians to closely monitor these patients and initiate effective preventive and therapeutic approaches to lessen the incidence of AKI. Several risk prediction models and risk assessment scores have been developed for CSA-AKI. However, the definition of AKI and the variables utilized in these risk scores differ, making general utility complex. Recently, the utility of artificial intelligence coupled with machine learning, has generated much interest and many studies in clinical medicine, including CSA-AKI. In this article, we discussed the evolution of models established by machine learning approaches to predict CSA-AKI.
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Affiliation(s)
- Charat Thongprayoon
- Division of Nephrology, Department of Medicine, Mayo Clinic, Rochester, MN 55905, USA;
| | - Panupong Hansrivijit
- Department of Internal Medicine, University of Pittsburgh Medical Center Pinnacle, Harrisburg, PA 17105, USA;
| | - Tarun Bathini
- Department of Internal Medicine, University of Arizona, Tucson, AZ 85724, USA;
| | | | - Poemlarp Mekraksakit
- Department of Internal Medicine, Texas Tech University Health Sciences Center, Lubbock, TX 79424, USA;
| | - Wisit Kaewput
- Department of Military and Community Medicine, Phramongkutklao College of Medicine, Bangkok 10400, Thailand;
| | - Wisit Cheungpasitporn
- Division of Nephrology, Department of Medicine, University of Mississippi Medical Center, Jackson, MS 39216, USA
- Correspondence: ; Tel.: +1-601-984-5670; Fax: +1-601-984-5765
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Cao Y, Montgomery S, Ottosson J, Näslund E, Stenberg E. Deep Learning Neural Networks to Predict Serious Complications After Bariatric Surgery: Analysis of Scandinavian Obesity Surgery Registry Data. JMIR Med Inform 2020; 8:e15992. [PMID: 32383681 PMCID: PMC7244994 DOI: 10.2196/15992] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2019] [Revised: 01/07/2020] [Accepted: 02/07/2020] [Indexed: 12/13/2022] Open
Abstract
Background Obesity is one of today’s most visible public health problems worldwide. Although modern bariatric surgery is ostensibly considered safe, serious complications and mortality still occur in some patients. Objective This study aimed to explore whether serious postoperative complications of bariatric surgery recorded in a national quality registry can be predicted preoperatively using deep learning methods. Methods Patients who were registered in the Scandinavian Obesity Surgery Registry (SOReg) between 2010 and 2015 were included in this study. The patients who underwent a bariatric procedure between 2010 and 2014 were used as training data, and those who underwent a bariatric procedure in 2015 were used as test data. Postoperative complications were graded according to the Clavien-Dindo classification, and complications requiring intervention under general anesthesia or resulting in organ failure or death were considered serious. Three supervised deep learning neural networks were applied and compared in our study: multilayer perceptron (MLP), convolutional neural network (CNN), and recurrent neural network (RNN). The synthetic minority oversampling technique (SMOTE) was used to artificially augment the patients with serious complications. The performances of the neural networks were evaluated using accuracy, sensitivity, specificity, Matthews correlation coefficient, and area under the receiver operating characteristic curve. Results In total, 37,811 and 6250 patients were used as the training data and test data, with incidence rates of serious complication of 3.2% (1220/37,811) and 3.0% (188/6250), respectively. When trained using the SMOTE data, the MLP appeared to have a desirable performance, with an area under curve (AUC) of 0.84 (95% CI 0.83-0.85). However, its performance was low for the test data, with an AUC of 0.54 (95% CI 0.53-0.55). The performance of CNN was similar to that of MLP. It generated AUCs of 0.79 (95% CI 0.78-0.80) and 0.57 (95% CI 0.59-0.61) for the SMOTE data and test data, respectively. Compared with the MLP and CNN, the RNN showed worse performance, with AUCs of 0.65 (95% CI 0.64-0.66) and 0.55 (95% CI 0.53-0.57) for the SMOTE data and test data, respectively. Conclusions MLP and CNN showed improved, but limited, ability for predicting the postoperative serious complications after bariatric surgery in the Scandinavian Obesity Surgery Registry data. However, the overfitting issue is still apparent and needs to be overcome by incorporating intra- and perioperative information.
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Affiliation(s)
- Yang Cao
- Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden
| | - Scott Montgomery
- Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden.,Clinical Epidemiology Division, Department of Medicine, Karolinska Institutet, Stockholm, Sweden.,Department of Epidemiology and Public Health, University College London, London, United Kingdom
| | - Johan Ottosson
- Department of Surgery, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
| | - Erik Näslund
- Division of Surgery, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden
| | - Erik Stenberg
- Department of Surgery, Faculty of Medicine and Health, Örebro University, Örebro, Sweden
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Elias K, Hedberg J, Sundbom M. Prevalence and impact of acid-related symptoms and diarrhea in patients undergoing Roux-en-Y gastric bypass, sleeve gastrectomy, and biliopancreatic diversion with duodenal switch. Surg Obes Relat Dis 2020; 16:520-527. [DOI: 10.1016/j.soard.2019.12.020] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/16/2019] [Revised: 11/03/2019] [Accepted: 12/15/2019] [Indexed: 02/07/2023]
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Cao Y, Fang X, Ottosson J, Näslund E, Stenberg E. A Comparative Study of Machine Learning Algorithms in Predicting Severe Complications after Bariatric Surgery. J Clin Med 2019; 8:jcm8050668. [PMID: 31083643 PMCID: PMC6571760 DOI: 10.3390/jcm8050668] [Citation(s) in RCA: 33] [Impact Index Per Article: 6.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/03/2019] [Revised: 05/08/2019] [Accepted: 05/10/2019] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Severe obesity is a global public health threat of growing proportions. Accurate models to predict severe postoperative complications could be of value in the preoperative assessment of potential candidates for bariatric surgery. So far, traditional statistical methods have failed to produce high accuracy. We aimed to find a useful machine learning (ML) algorithm to predict the risk for severe complication after bariatric surgery. METHODS We trained and compared 29 supervised ML algorithms using information from 37,811 patients that operated with a bariatric surgical procedure between 2010 and 2014 in Sweden. The algorithms were then tested on 6250 patients operated in 2015. We performed the synthetic minority oversampling technique tackling the issue that only 3% of patients experienced severe complications. RESULTS Most of the ML algorithms showed high accuracy (>90%) and specificity (>90%) in both the training and test data. However, none of the algorithms achieved an acceptable sensitivity in the test data. We also tried to tune the hyperparameters of the algorithms to maximize sensitivity, but did not yet identify one with a high enough sensitivity that can be used in clinical praxis in bariatric surgery. However, a minor, but perceptible, improvement in deep neural network (NN) ML was found. CONCLUSION In predicting the severe postoperative complication among the bariatric surgery patients, ensemble algorithms outperform base algorithms. When compared to other ML algorithms, deep NN has the potential to improve the accuracy and it deserves further investigation. The oversampling technique should be considered in the context of imbalanced data where the number of the interested outcome is relatively small.
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Affiliation(s)
- Yang Cao
- Clinical Epidemiology and Biostatistics, School of Medical Sciences, Örebro University, Örebro, Sweden.
| | - Xin Fang
- Unit of Biostatistics, Institute of Environmental Medicine, Karolinska Institutet, Stockholm, Sweden.
| | - Johan Ottosson
- Department of Surgery, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
| | - Erik Näslund
- Division of Surgery, Department of Clinical Sciences, Danderyd Hospital, Karolinska Institutet, Stockholm, Sweden.
| | - Erik Stenberg
- Department of Surgery, Faculty of Medicine and Health, Örebro University, Örebro, Sweden.
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Stenberg E, Persson C, Näslund E, Ottosson J, Sundbom M, Szabo E, Näslund I. The impact of socioeconomic factors on the early postoperative complication rate after laparoscopic gastric bypass surgery: A register-based cohort study. Surg Obes Relat Dis 2019; 15:575-581. [DOI: 10.1016/j.soard.2019.01.025] [Citation(s) in RCA: 11] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2018] [Revised: 11/23/2018] [Accepted: 01/28/2019] [Indexed: 12/24/2022]
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30
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Impact of mesenteric defect closure technique on complications after gastric bypass. Langenbecks Arch Surg 2018; 403:481-486. [PMID: 29858618 PMCID: PMC6013510 DOI: 10.1007/s00423-018-1684-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/25/2018] [Accepted: 05/24/2018] [Indexed: 01/06/2023]
Abstract
Background Closure of mesenteric defects during laparoscopic gastric bypass surgery markedly reduces the risk for small bowel obstruction due to internal hernia. However, this procedure is associated with an increased risk for early small bowel obstruction and pulmonary complication. The purpose of the present study was to evaluate whether the learning curve and subsequent adaptions made to the technique have had an effect on the risk for complications. Methods The results of patients operated with a primary laparoscopic gastric bypass procedure, including closure of the mesenteric defects with sutures, during a period soon after introduction (January 1, 2010–December 31, 2011) were compared to those of patients operated recently (January 1, 2014–June 30, 2017). Data were retrieved from the Scandinavian Obesity Surgery Registry (SOReg). The main outcome was reoperation for small bowel obstruction within 30 days after surgery. Results A total of 5444 patients were included in the first group (period 1), and 1908 in the second group (period 2). Thirty-day follow-up rates were 97.1 and 97.5% respectively. The risk for early (within 30 days) small bowel obstruction was lower in period 2 than in period 1 (13/1860, 0.7% vs. 67/5285, 1.3%, OR 0.55 (0.30–0.99), p = 0.045). The risk for pulmonary complication was also reduced (5/1860, 0.3%, vs. 41/5285, 0.8%, OR 0.34 (0.14–0.87), p = 0.019). Conclusion Closure of mesenteric defects during laparoscopic gastric bypass surgery can be performed safely and should be viewed as a routine part of that operation.
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