1
|
Jaraczewski TJ, SenthilKumar G, Ramamurthi A, Nimmer K, Yang X, Kothari AN. Teaming with artificial intelligence to support global cancer surgical care. J Surg Oncol 2023; 128:943-946. [PMID: 37818910 DOI: 10.1002/jso.27442] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 09/02/2023] [Indexed: 10/13/2023]
Affiliation(s)
- Taylor J Jaraczewski
- Department of Surgery, Division of Surgical Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Gopika SenthilKumar
- Department of Surgery, Division of Surgical Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
- Department of Physiology and Anesthesiology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Adhitya Ramamurthi
- Department of Surgery, Division of Surgical Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Kaitlyn Nimmer
- Department of Surgery, Division of Surgical Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Xin Yang
- Clinical and Translational Science Institute of Southeast Wisconsin, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| | - Anai N Kothari
- Department of Surgery, Division of Surgical Oncology, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
- Clinical and Translational Science Institute of Southeast Wisconsin, Medical College of Wisconsin, Milwaukee, Wisconsin, USA
| |
Collapse
|
2
|
Spence RT, Guidolin K, Quereshy FA, Chadi SA, Chang DC, Hutter MM. External validation of the Codman score in colorectal surgery: a pragmatic tool to drive quality improvement. Colorectal Dis 2023. [PMID: 36965098 DOI: 10.1111/codi.16547] [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: 10/03/2022] [Revised: 12/23/2022] [Accepted: 01/23/2023] [Indexed: 03/27/2023]
Abstract
AIM The simple six-variable Codman score is a tool designed to reduce the complexity of contemporary risk-adjusted postoperative mortality rate predictions. We sought to externally validate the Codman score in colorectal surgery. METHODS The American College of Surgeons National Surgical Quality Improvement Program (ACS NSQIP) participant user file and colectomy targeted dataset of 2020 were merged. A Codman score (composed of six variables: age, American Society of Anesthesiologists score, emergency status, degree of sepsis, functional status and preoperative blood transfusion) was assigned to every patient. The primary outcome was in-hospital mortality and secondary outcome was morbidity at 30 days. Logistic regression analyses were performed using the Codman score and the ACS NSQIP mortality and morbidity algorithms as independent variables for the primary and secondary outcomes. The predictive performance of discrimination area under receiver operating curve (AUC) and calibration of the Codman score and these algorithms were compared. RESULTS A total of 40 589 patients were included and a Codman score was generated for 40 557 (99.02%) patients. The median Codman score was 3 (interquartile range 1-4). To predict mortality, the Codman score had an AUC of 0.92 (95% CI 0.91-0.93) compared to the NSQIP mortality score 0.93 (95% CI 0.92-0.94). To predict morbidity, the Codman score had an AUC of 0.68 (95% CI 0.66-0.68) compared to the NSQIP morbidity score 0.72 (95% CI 0.71-0.73). When body mass index and surgical approach was added to the Codman score, the performance was no different to the NSQIP morbidity score. The calibration of observed versus expected predictions was almost perfect for both the morbidity and mortality NSQIP predictions, and only well fitted for Codman scores of less than 4 and greater than 7. CONCLUSION We propose that the six-variable Codman score is an efficient and actionable method for generating validated risk-adjusted outcome predictions and comparative benchmarks to drive quality improvement in colorectal surgery.
Collapse
Affiliation(s)
- Richard T Spence
- Department of Surgery, Dalhousie University, Halifax, Nova Scotia, Canada
| | - Keegan Guidolin
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
| | - Fayez A Quereshy
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- University Health Network and Princess Margaret Hospital, Toronto, Ontario, Canada
| | - Sami A Chadi
- Department of Surgery, University of Toronto, Toronto, Ontario, Canada
- Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
- University Health Network and Princess Margaret Hospital, Toronto, Ontario, Canada
| | - David C Chang
- Department of General Surgery, Codman Center for Clinical Effectiveness in Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| | - Matthew M Hutter
- Department of General Surgery, Codman Center for Clinical Effectiveness in Surgery, Massachusetts General Hospital, Boston, Massachusetts, USA
| |
Collapse
|
3
|
Kluyts HL, Bedwell GJ, Bedada AG, Fadalla T, Hewitt-Smith A, Mbwele BA, Mrara B, Omigbodun A, Omoshoro-Jones J, Turton EW, Belachew FK, Chu K, Cloete E, Ekwen G, Elfagieh MA, Elfiky M, Maimbo M, Morais A, Mpirimbanyi C, Munlemvo D, Ndarukwa P, Smalle I, Torborg A, Ulisubisya M, Fawzy M, Gobin V, Mbeki M, Ngumi Z, Patel-Mujajati U, Sama HD, Tumukunde J, Antwi-Kusi A, Basenaro A, Lamacraft G, Madzimbamuto F, Maswime S, Msosa V, Mulwafu W, Youssouf C, Pearse R, Biccard BM. Determining the Minimum Dataset for Surgical Patients in Africa: A Delphi Study. World J Surg 2023; 47:581-592. [PMID: 36380103 DOI: 10.1007/s00268-022-06815-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 10/15/2022] [Indexed: 11/16/2022]
Abstract
BACKGROUND It is often difficult for clinicians in African low- and middle-income countries middle-income countries to access useful aggregated data to identify areas for quality improvement. The aim of this Delphi study was to develop a standardised perioperative dataset for use in a registry. METHODS A Delphi method was followed to achieve consensus on the data points to include in a minimum perioperative dataset. The study consisted of two electronic surveys, followed by an online discussion and a final electronic survey (four Rounds). RESULTS Forty-one members of the African Perioperative Research Group participated in the process. Forty data points were deemed important and feasible to include in a minimum dataset for electronic capturing during the perioperative workflow by clinicians. A smaller dataset consisting of eight variables to define risk-adjusted perioperative mortality rate was also described. CONCLUSIONS The minimum perioperative dataset can be used in a collaborative effort to establish a resource accessible to African clinicians in improving quality of care.
Collapse
Affiliation(s)
- Hyla-Louise Kluyts
- Department Anaesthesiology, Dr George Mukhari Academic Hospital, Sefako Makgatho Health Sciences University, Molotlegi Street, Ga-Rankuwa, Pretoria, South Africa.
| | - Gillian J Bedwell
- Department of Anaesthesia and Perioperative Medicine, Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
| | - Alemayehu G Bedada
- Department of Surgery, Faculty of Medicine, Princess Marina Hospital, University of Botswana, Corner of Notwane and Mobuto Road, Gaborone, Botswana
| | - Tarig Fadalla
- Ribat Neurospine Center, Ribat University Hospital, The National Ribat University, Nile Street Burri, Khartoum, Sudan
| | - Adam Hewitt-Smith
- Department of Anaesthesia and Critical Care, Faculty of Health Sciences, Busitema University, Mbale Campus, Mbale, Uganda
| | - Bernard A Mbwele
- Department of Epidemiology, Mbeya Zonal Referral Hospital, University of Dar Es Salaam, Mbeya, Tanzania
| | - Busisiwe Mrara
- Department of Anaesthesiology, Nelson Mandela Academic Hospital, Walter Sisulu University, Sissons Street Campus, Fortgale, Mthatha, Eastern Cape, South Africa
| | - Akinyinka Omigbodun
- College of Medicine, University College Hospital Ibadan, University of Ibadan, Ibadan, Nigeria
| | - Jones Omoshoro-Jones
- Department of Surgery, Chris Hani-Baragwanath Academic Hospital, Faculty of Health Sciences, School of Clinical Medicine, University of the Witwatersrand, Johannesburg, South Africa
| | - Edwin W Turton
- Faculty of Health Sciences, University of the Free State and Pelonomi Tertiary Hospital, PO Box 339 (G67), Bloemfontein, South Africa
| | | | - Kathryn Chu
- Centre for Global Surgery, Department of Global Health, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Esther Cloete
- Department of Anaesthesia and Perioperative Medicine, Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
| | - Gerald Ekwen
- JJ Dosen County Referral Hospital, Maryland, Liberia
| | | | | | | | - Atilio Morais
- Departamento de Cirurgias Faculdade De Medicina, College of Cardiovascular and Thoracic Surgery, Hospital Central de Maputo, Universidade Eduardo Mondlane, Maputo, Mozambique
| | | | - Dolly Munlemvo
- University Hospital of Kinshasa, Kinshasa, Democratic Republic of the Congo
| | - Pisirai Ndarukwa
- Bindura University of Science Education, Bindura, Zimbabwe.,School of Nursing and Public Health, University of Kwa-Zulu Natal, Durban, South Africa
| | - Isaac Smalle
- College of Medicine and Allied Health Sciences, University of Sierra Leone, Connaught Hospital, Freetown, Sierra Leone
| | - Alexandra Torborg
- Discipline of Anaesthesiology and Critical Care, Nelson R Mandela School of Medicine, University of Kwa-Zulu Natal, Durban, South Africa
| | - Mpoki Ulisubisya
- Hubert Kairuki Memorial University, Mbeya Zonal Referral Hospital, Mbeya, Tanzania
| | - Maher Fawzy
- Faculty of Medicine, Cairo University Hospitals, Cairo University, Cairo, Egypt
| | - Veekash Gobin
- Jawaharall Nehru Hospital, Ministry of Health and Wellness, Rose Belle, Mauritius
| | - Motselisi Mbeki
- Kalafong Provincial Tertiary Hospital, University of Pretoria, Pretoria, South Africa
| | - Zipporah Ngumi
- School of Medicine, Kenyatta National Hospital, University of Nairobi, Nairobi, Kenya
| | | | - Hamza D Sama
- Anesthesia Resuscitation and Critical Care Medicine, Sylvanus Olympio University Teaching Hospital, Lomé, Togo
| | - Janat Tumukunde
- College of Health Sciences, Makerere University, Kampala, Uganda
| | | | - Apollo Basenaro
- MPH Ministry of Health and Social Services, Windhoek, Namibia
| | | | | | - Salome Maswime
- Department of Global Surgery, University of Cape Town, Cape Town, South Africa
| | | | - Wakisa Mulwafu
- Kamuzu University of Health Sciences, Queen Elizabeth Central Hospital Blantyre, Blantyre, Malawi
| | | | - Rupert Pearse
- Faculty of Medicine and Dentistry, Queen Mary University of London, London, UK
| | - Bruce M Biccard
- Department of Anaesthesia and Perioperative Medicine, Groote Schuur Hospital, University of Cape Town, Cape Town, South Africa
| |
Collapse
|
4
|
Look in the Mirror, Not Out the Window. ANNALS OF SURGERY OPEN 2022; 3:e184. [PMID: 36199485 PMCID: PMC9508963 DOI: 10.1097/as9.0000000000000184] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/23/2022] [Accepted: 06/14/2022] [Indexed: 11/26/2022] Open
Abstract
Over the past few decades, institutions have developed complex systems to compare themselves to others with the goal of improving healthcare quality. This process of comparison to others, called external benchmarking, has become the standard approach for quality improvement. However, external benchmarking is resource intensive, may not be flexible enough to focus on problems unique to individual institutions, and may lead to complacency for institutions ranking near the top of the quality bell curve for the measured metrics. Our singular focus on external benchmarking could also divert resources from other approaches. Here, we describe how the use of internal benchmarking, in which an institution focuses on improving their own processes over time, can offer unique advantages as well as offset the limitations of external benchmarking. We advocate for investment in both internal and external benchmarking as complimentary tools to improve healthcare quality. Mini-abstract: External benchmarking, the most common approach to benchmarking today, compares outcomes across a group to improve performance. This article discusses internal benchmarking, a complementary approach with several strengths—including reduced data burden, selection of tailored metrics, motivation to improve for all performers, and incentive to look inward for solutions.
Collapse
|
5
|
Saarinen IH, Malmivaara A, Huhtala H, Kaipia A. Creating an inexpensive hospital-wide surgical complication register for performance monitoring: a cohort study. BMJ Open Qual 2022; 11:bmjoq-2021-001804. [PMID: 35788053 PMCID: PMC9255416 DOI: 10.1136/bmjoq-2021-001804] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/05/2022] [Accepted: 05/31/2022] [Indexed: 11/04/2022] Open
Abstract
ObjectivesBasic tools that measure a hospital’s performance are required in order to benchmark or compare hospitals, but multispecialty institutional registries are rarely reported, and there is no consensus on their standard definitions and methodology. This study aimed to describe the setting up and first results of a hospital-wide surgical complication register that uses a minimal set of patient-related risk factors based on bedside data and produces outcomes data based on severity of complications.DesignCohort study.SettingPerioperative data related to all adult surgical procedures in a tertiary referral centre in Finland for 3 years (2016–2018) were included in the study. Complications were recorded according to a modified Clavien-Dindo classification, and the preoperative risk factors were compiled based on the literature and coded as numerical measures. The associations of preoperative risk factors with postoperative complications were analysed using the χ2 test or Fisher’s exact test.ResultsIn total, 19 158 operations were performed between 2016 and 2018. Data on complications (Clavien 0–9) were recorded for 4529 surgical patients (23.6%), and 779 complications were reported (Clavien 1–9), leading to an overall complication rate of 17.2%. Of these, 4.6% were graded as major (Clavien 4–7). Patient-related risk factors with the strongest association with complications were growing American Society of Anesthesiologists Physical Status Classification System score (p<0.001), growing Charlson Index (p<0.001), poor nutritional status (Nutritional Risk Screening 2002), p=0.041) and urgency of surgery (p<0.001).ConclusionsWe describe an inexpensive hospital-wide surgical complication monitoring system that can produce valid numerical data for monitoring risk-adjusted surgical quality. The results showed that only a few patient-related risk factors were sufficient to account for the case mix.
Collapse
Affiliation(s)
- Ira H Saarinen
- Surgery, Satakunnan Sairaanhoitopiiri, Pori, Finland
- Surgery, Etelä-Pohjanmaan Sairaanhoitopiiri, Seinajoki, Finland
| | - Antti Malmivaara
- Department of Public Health and Welfare, Finnish Institute for Health and Welfare, Helsinki, Finland
| | - Heini Huhtala
- Faculty of Social Sciences, Tampere University, Tampere, Finland
| | - Antti Kaipia
- Urology, Tampere University Hospital, Tampere, Finland
| |
Collapse
|
6
|
McAuliffe PB, Hsu JY, Broach RB, Borovskiy Y, Christopher AN, Morris MP, Fischer JP. Systematic variable reduction for simplification of incisional hernia risk prediction instruments. Am J Surg 2022; 224:576-583. [DOI: 10.1016/j.amjsurg.2022.03.003] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2022] [Revised: 02/14/2022] [Accepted: 03/01/2022] [Indexed: 11/29/2022]
|
7
|
Ullrich SJ, Kisa P, Muzira A, Kakembo N, Nabukenya M, Tumukunde J, Sekabira J, Chang DC, Ozgediz D. Pediatric surgical quality improvement in low- and middle-income countries: What data to collect? Surgery 2022; 171:1067-1072. [PMID: 35078626 DOI: 10.1016/j.surg.2021.09.010] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2021] [Revised: 09/13/2021] [Accepted: 09/14/2021] [Indexed: 11/16/2022]
Abstract
BACKGROUND As surgical access expands in low- and middle-income countries, risk-adjusted outcomes data are needed to measure and improve surgical quality. Existing data collection tools in high-income countries are complex and may be burdensome to implement in low and middle income countries. This study determined the minimum dataset needed for adequate risk adjustment to predict perioperative mortality using data collected in a low- and middle-income countries. METHODS All patients admitted to the pediatric surgery ward at Mulago National Referral Hospital in Kampala, Uganda, from January 1, 2014 through December 31, 2018 were included. Studies were performed modelling the effects of reducing data granularity and reducing number of variables on the area under the receiver operating curve. RESULTS Of the 3,194 patients included, 1,941(61%) were male, 957(30%) were neonates, 1,714 (54%) had an operation, and the overall mortality rate was 14%. Granularity reduction analyses found that measuring age in ranges was equivalent to recording age in days (area under the receiver operating curve = 0.776; 95% confidence interval, 0.754%-0.798%, vs 0.815, 95% confidence interval, 0.794%-0.837%). Variable reduction analyses found that models with 3 predictor variables (diagnosis, procedure, and district) reached a maximum area under the receiver operating curve of 0.915 (95% confidence interval, 0.903%-0.928%), which was equivalent to the model using all available predictor variables (area under the receiver operating curve = 0.932; 95% confidence interval, 0.922%-0.943%). For all 3-variable models, the primary diagnosis contributed most to predictive ability (P < .001). CONCLUSION Effective risk adjustment for perioperative mortality can be performed in low and middle income countries using minimal, objective variables often already part of the patient's medical record. This approach can be used by clinicians, hospital administrators, and policymakers low- and middle-income countries looking to begin data collection to track and improve patient outcomes.
Collapse
Affiliation(s)
- Sarah J Ullrich
- Department of Surgery, Yale University School of Medicine, New Haven, CT.
| | - Phyllis Kisa
- Department of Surgery, Mulago National Referral Hospital, Kampala, Uganda
| | - Arlene Muzira
- Department of Surgery, Mulago National Referral Hospital, Kampala, Uganda
| | - Nasser Kakembo
- Department of Surgery, Mulago National Referral Hospital, Kampala, Uganda
| | - Mary Nabukenya
- Department of Anesthesia, Mulago National Referral Hospital, Kampala, Uganda
| | - Janat Tumukunde
- Department of Anesthesia, Mulago National Referral Hospital, Kampala, Uganda
| | - John Sekabira
- Department of Surgery, Mulago National Referral Hospital, Kampala, Uganda
| | - David C Chang
- Department of Surgery, Massachusetts General Hospital, Boston, MA
| | - Doruk Ozgediz
- Department of Surgery, University of California San Francisco, San Francisco, CA
| |
Collapse
|
8
|
Firth PG, Ngonzi J, Mushagara R, Musinguzi N, Liu C, Boatin AA, Mugabi W, Kayaga D, Naturinda P, Twesigye D, Sanyu F, Mugyenyi G, Ttendo SS. The structure, function and implementation of an outcomes database at a Ugandan secondary hospital: the Mbarara Surgical Services Quality Assurance Database. SOUTHERN AFRICAN JOURNAL OF ANAESTHESIA AND ANALGESIA 2022. [DOI: 10.36303/sajaa.2022.28.1.2637] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/05/2022]
Affiliation(s)
- PG Firth
- Department of Anesthesia, Critical Care and Pain Medicine, Massachusetts General Hospital,
United States of America
| | - J Ngonzi
- Department of Obstetrics and Gynaecology, Mbarara Regional Referral Hospital,
Uganda
| | - R Mushagara
- Harvard-MUST Global Health Collaborative, Mbarara Regional Referral Hospital,
Uganda
| | - N Musinguzi
- Harvard-MUST Global Health Collaborative, Mbarara Regional Referral Hospital,
Uganda
| | - C Liu
- Department of Surgery, Lucille Packard Children’s Hospital at Stanford,
United States of America
| | - AA Boatin
- Department of Obstetrics and Gynecology, Massachusetts General Hospital,
United States of America
| | - W Mugabi
- Harvard-MUST Global Health Collaborative, Mbarara Regional Referral Hospital,
Uganda
| | - D Kayaga
- Harvard-MUST Global Health Collaborative, Mbarara Regional Referral Hospital,
Uganda
| | - P Naturinda
- Harvard-MUST Global Health Collaborative, Mbarara Regional Referral Hospital,
Uganda
| | - D Twesigye
- Department of Surgery, Mbarara Regional Referral Hospital,
Uganda
| | - F Sanyu
- Medical Records Department, Mbarara Regional Referral Hospital,
Uganda
| | - G Mugyenyi
- Department of Obstetrics and Gynaecology, Mbarara Regional Referral Hospital,
Uganda
| | - SS Ttendo
- Department of Anaesthesia and Critical Care, Mbarara Regional Referral Hospital,
Uganda
| | | |
Collapse
|
9
|
Segar MW, Jaeger BC, Patel KV, Nambi V, Ndumele CE, Correa A, Butler J, Chandra A, Ayers C, Rao S, Lewis AA, Raffield LM, Rodriguez CJ, Michos ED, Ballantyne CM, Hall ME, Mentz RJ, de Lemos JA, Pandey A. Development and Validation of Machine Learning-Based Race-Specific Models to Predict 10-Year Risk of Heart Failure: A Multicohort Analysis. Circulation 2021; 143:2370-2383. [PMID: 33845593 PMCID: PMC9976274 DOI: 10.1161/circulationaha.120.053134] [Citation(s) in RCA: 46] [Impact Index Per Article: 15.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/08/2020] [Accepted: 02/23/2021] [Indexed: 12/30/2022]
Abstract
BACKGROUND Heart failure (HF) risk and the underlying risk factors vary by race. Traditional models for HF risk prediction treat race as a covariate in risk prediction and do not account for significant parameters such as cardiac biomarkers. Machine learning (ML) may offer advantages over traditional modeling techniques to develop race-specific HF risk prediction models and to elucidate important contributors of HF development across races. METHODS We performed a retrospective analysis of 4 large, community cohort studies (ARIC [Atherosclerosis Risk in Communities], DHS [Dallas Heart Study], JHS [Jackson Heart Study], and MESA [Multi-Ethnic Study of Atherosclerosis]) with adjudicated HF events. The study included participants who were >40 years of age and free of HF at baseline. Race-specific ML models for HF risk prediction were developed in the JHS cohort (for Black race-specific model) and White adults from ARIC (for White race-specific model). The models included 39 candidate variables across demographic, anthropometric, medical history, laboratory, and electrocardiographic domains. The ML models were externally validated and compared with prior established traditional and non-race-specific ML models in race-specific subgroups of the pooled MESA/DHS cohort and Black participants of ARIC. The Harrell C-index and Greenwood-Nam-D'Agostino χ2 tests were used to assess discrimination and calibration, respectively. RESULTS The ML models had excellent discrimination in the derivation cohorts for Black (n=4141 in JHS, C-index=0.88) and White (n=7858 in ARIC, C-index=0.89) participants. In the external validation cohorts, the race-specific ML model demonstrated adequate calibration and superior discrimination (Black individuals, C-index=0.80-0.83; White individuals, C-index=0.82) compared with established HF risk models or with non-race-specific ML models derived with race included as a covariate. Among the risk factors, natriuretic peptide levels were the most important predictor of HF risk across both races, followed by troponin levels in Black and ECG-based Cornell voltage in White individuals. Other key predictors of HF risk among Black individuals were glycemic parameters and socioeconomic factors. In contrast, prevalent cardiovascular disease and traditional cardiovascular risk factors were stronger predictors of HF risk in White adults. CONCLUSIONS Race-specific and ML-based HF risk models that integrate clinical, laboratory, and biomarker data demonstrated superior performance compared with traditional HF risk and non-race-specific ML models. This approach identifies distinct race-specific contributors of HF.
Collapse
Affiliation(s)
- Matthew W. Segar
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Parkland Health and Hospital System, Dallas, TX, USA
| | - Byron C. Jaeger
- Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA
| | - Kershaw V. Patel
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Department of Cardiology, Houston Methodist DeBakey Heart & Vascular Center, Houston, TX
| | - Vijay Nambi
- Michael E DeBakey Veterans Affairs Hospital and Baylor College of Medicine, Houston, TX, USA
- Section of Cardiology and Cardiovascular Research, Department of Medicine, Baylor College of Medicine, Houston, TX, USA
| | - Chiadi E. Ndumele
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Adolfo Correa
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Javed Butler
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Alvin Chandra
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Colby Ayers
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Shreya Rao
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
- Parkland Health and Hospital System, Dallas, TX, USA
| | - Alana A. Lewis
- Division of Cardiology, Northwestern University, Chicago, IL, UA
| | - Laura M. Raffield
- Department of Genetics, University of North Carolina, Chapel Hill, NC, USA
| | - Carlos J. Rodriguez
- Departments of Medicine, Epidemiology and Population Health, Albert Einstein College of Medicine, Bronx, New York
| | - Erin D. Michos
- Division of Cardiology, Department of Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Christie M. Ballantyne
- Michael E DeBakey Veterans Affairs Hospital and Baylor College of Medicine, Houston, TX, USA
| | - Michael E. Hall
- Department of Medicine, University of Mississippi Medical Center, Jackson, MS, USA
| | - Robert J. Mentz
- Division of Cardiology, Duke Clinical Research Institute, Durham, North Carolina
| | - James A. de Lemos
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| | - Ambarish Pandey
- Division of Cardiology, Department of Internal Medicine, University of Texas Southwestern Medical Center, Dallas, TX, USA
| |
Collapse
|
10
|
McCrum ML, Valmont T, Price RR. Developing a Surgical Quality Improvement Program for Resource-Limited Settings. JAMA Surg 2020; 155:1160-1161. [PMID: 33112387 DOI: 10.1001/jamasurg.2020.4347] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/07/2023]
Affiliation(s)
- Marta L McCrum
- Division of General Surgery, University of Utah, Salt Lake City.,Center for Global Surgery, Department of Surgery, University of Utah, Salt Lake City.,Division of General Surgery, University of Utah, Salt Lake City
| | | | - Raymond R Price
- Center for Global Surgery, Department of Surgery, University of Utah, Salt Lake City.,Division of General Surgery, University of Utah, Salt Lake City
| |
Collapse
|
11
|
Burden of emergency pediatric surgical procedures on surgical capacity in Uganda: a new metric for health system performance. Surgery 2020; 167:668-674. [DOI: 10.1016/j.surg.2019.12.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/31/2019] [Revised: 11/17/2019] [Accepted: 12/04/2019] [Indexed: 11/24/2022]
|
12
|
Cheung M, Kakembo N, Rizgar N, Grabski D, Ullrich S, Muzira A, Kisa P, Sekabira J, Ozgediz D. Epidemiology and mortality of pediatric surgical conditions: insights from a tertiary center in Uganda. Pediatr Surg Int 2019; 35:1279-1289. [PMID: 31324976 DOI: 10.1007/s00383-019-04520-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Accepted: 07/01/2019] [Indexed: 02/07/2023]
Abstract
INTRODUCTION/PURPOSE The burden of pediatric surgical disease is largely unknown in low- and middle-income countries such as Uganda where access to care is limited. METHODS Implementation of a locally led database in January 2012 at a Ugandan tertiary referral hospital, and review of 3465 prospectively collected pediatric surgical admissions from January 2012 to August 2016. RESULTS 2090 children (60.3%) underwent surgery during admission. 59% were male and 41% female. 28.6% of admissions were in neonates and 50.4% were in children less than 1 year old. Congenital anomalies including Hirschsprung's, anorectal malformations, intestinal atresias, omphalocele, and gastroschisis were the most common diagnoses (38.6%) followed by infections (15.0%) and tumors (8.6%). Mortality rates were substantially higher than those of high-income countries; for example, gastroschisis and intussusception had mortality rates of 90.1% and 19.7%, respectively. Post-operative mortality was highest in the congenital anomalies group (15.0%). CONCLUSION There is a high burden of infant congenital anomalies with higher mortality rates compared to high-income countries. The unit performs primarily specialized procedures appropriate for a tertiary center. We hope that these data will facilitate evaluation of ongoing quality improvement and capacity-building initiatives.
Collapse
Affiliation(s)
- Maija Cheung
- Department of Surgery, Yale University School of Medicine, 330 Cedar Street, FMB 107, New Haven, CT, 06510, USA.
| | - Nasser Kakembo
- Department of Surgery, Makerere University, Mulago Hospital, Kampala, Uganda
| | - Nensi Rizgar
- Yale University School of Medicine, New Haven, CT, USA
| | - David Grabski
- Department of Surgery, University of Virginia School of Medicine, Charlottesville, VA, USA
| | - Sarah Ullrich
- Department of Surgery, Yale University School of Medicine, 330 Cedar Street, FMB 107, New Haven, CT, 06510, USA
| | - Arlene Muzira
- Department of Surgery, Makerere University, Mulago Hospital, Kampala, Uganda
| | - Phyllis Kisa
- Department of Surgery, Makerere University, Mulago Hospital, Kampala, Uganda
| | - John Sekabira
- Department of Surgery, Makerere University, Mulago Hospital, Kampala, Uganda
| | - Doruk Ozgediz
- Department of Surgery, Yale University School of Medicine, 330 Cedar Street, FMB 107, New Haven, CT, 06510, USA
| |
Collapse
|
13
|
Katz MG, Price RR, Nunez JM. Local Research Catalyzes National Surgical Planning Comment on "Global Surgery - Informing National Strategies for Scaling Up Surgery in Sub-Saharan Africa". Int J Health Policy Manag 2018; 7:1058-1060. [PMID: 30624880 PMCID: PMC6326645 DOI: 10.15171/ijhpm.2018.78] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2018] [Accepted: 08/04/2018] [Indexed: 01/20/2023] Open
Abstract
In 2015 the Lancet Commission on Global Surgery (LCoGS) argued that surgical care is important to national health systems along with the economic viability of countries. Gajewski and colleagues outlined how the Commission’s blueprint has been implemented in sub-Saharan Africa, including two funded research projects that were integrated into national surgical plans. Here, we outline how the five processes proposed by Gajewski and colleagues are critical to integrate research, policy, and on-the-ground implementation. We also propose that, moving forward, the most pressing adjunct in many low- and middle-income countries (LMICs) may be a better characterization of rural surgical practices through rigorous research along with models that enable lessons to inform national policy.
Collapse
Affiliation(s)
- Micah G Katz
- Center for Global Surgery, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Raymond R Price
- Center for Global Surgery, University of Utah School of Medicine, Salt Lake City, UT, USA
| | - Jade M Nunez
- Center for Global Surgery, University of Utah School of Medicine, Salt Lake City, UT, USA
| |
Collapse
|
14
|
Spence RT, Chang DC, Kaafarani HMA, Panieri E, Anderson GA, Hutter MM. Derivation, Validation and Application of a Pragmatic Risk Prediction Index for Benchmarking of Surgical Outcomes. World J Surg 2018; 42:533-540. [PMID: 28795214 DOI: 10.1007/s00268-017-4177-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/13/2022]
Abstract
BACKGROUND Despite the existence of multiple validated risk assessment and quality benchmarking tools in surgery, their utility outside of high-income countries is limited. We sought to derive, validate and apply a scoring system that is both (1) feasible, and (2) reliably predicts mortality in a middle-income country (MIC) context. METHODS A 5-step methodology was used: (1) development of a de novo surgical outcomes database modeled around the American College of Surgeons' National Surgical Quality Improvement Program (ACS-NSQIP) in South Africa (SA dataset), (2) use of the resultant data to identify all predictors of in-hospital death with more than 90% capture indicating feasibility of collection, (3) use these predictors to derive and validate an integer-based score that reliably predicts in-hospital death in the 2012 ACS-NSQIP, (4) apply the score in the original SA dataset and demonstrate its performance, (5) identify threshold cutoffs of the score to prompt action and drive quality improvement. RESULTS Following step one-three above, the 13 point Codman's score was derived and validated on 211,737 and 109,079 patients, respectively, and includes: age 65 (1), partially or completely dependent functional status (1), preoperative transfusions ≥4 units (1), emergency operation (2), sepsis or septic shock (2) American Society of Anesthesia score ≥3 (3) and operative procedure (1-3). Application of the score to 373 patients in the SA dataset showed good discrimination and calibration to predict an in-hospital death. A Codman Score of 8 is an optimal cutoff point for defining expected and unexpected deaths. CONCLUSION We have designed a novel risk prediction score specific for a MIC context. The Codman Score can prove useful for both (1) preoperative decision-making and (2) benchmarking the quality of surgical care in MIC's.
Collapse
Affiliation(s)
- Richard T Spence
- Department of General Surgery, Codman Center for Clinical Effectiveness in Surgery, Massachusetts General Hospital, Boston, MA, USA. .,Department of Surgery, University of Cape Town, Cape Town, South Africa.
| | - David C Chang
- Department of General Surgery, Codman Center for Clinical Effectiveness in Surgery, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Haytham M A Kaafarani
- Department of General Surgery, Codman Center for Clinical Effectiveness in Surgery, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| | - Eugenio Panieri
- Department of Surgery, University of Cape Town, Cape Town, South Africa
| | | | - Matthew M Hutter
- Department of General Surgery, Codman Center for Clinical Effectiveness in Surgery, Massachusetts General Hospital, Boston, MA, USA.,Harvard Medical School, Boston, MA, USA
| |
Collapse
|
15
|
Marks IH, Fong ZV, Stapleton SM, Hung YC, Bababekov YJ, Chang DC. How Much Data are Good Enough? Using Simulation to Determine the Reliability of Estimating POMR for Resource-Constrained Settings. World J Surg 2018; 42:2344-2347. [DOI: 10.1007/s00268-018-4529-6] [Citation(s) in RCA: 5] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/18/2022]
|
16
|
Liu C, Kayima P, Riesel J, Situma M, Chang D, Firth P. Brief surgical procedure code lists for outcomes measurement and quality improvement in resource-limited settings. Surgery 2017; 162:1163-1176. [PMID: 28864101 DOI: 10.1016/j.surg.2017.07.002] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.1] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/13/2017] [Revised: 06/23/2017] [Accepted: 07/04/2017] [Indexed: 11/28/2022]
Abstract
BACKGROUND The lack of a classification system for surgical procedures in resource-limited settings hinders outcomes measurement and reporting. Existing procedure coding systems are prohibitively large and expensive to implement. We describe the creation and prospective validation of 3 brief procedure code lists applicable in low-resource settings, based on analysis of surgical procedures performed at Mbarara Regional Referral Hospital, Uganda's second largest public hospital. METHODS We reviewed operating room logbooks to identify all surgical operations performed at Mbarara Regional Referral Hospital during 2014. Based on the documented indication for surgery and procedure(s) performed, we assigned each operation up to 4 procedure codes from the International Classification of Diseases, 9th Revision, Clinical Modification. Coding of procedures was performed by 2 investigators, and a random 20% of procedures were coded by both investigators. These codes were aggregated to generate procedure code lists. RESULTS During 2014, 6,464 surgical procedures were performed at Mbarara Regional Referral Hospital, to which we assigned 435 unique procedure codes. Substantial inter-rater reliability was achieved (κ = 0.7037). The 111 most common procedure codes accounted for 90% of all codes assigned, 180 accounted for 95%, and 278 accounted for 98%. We considered these sets of codes as 3 procedure code lists. In a prospective validation, we found that these lists described 83.2%, 89.2%, and 92.6% of surgical procedures performed at Mbarara Regional Referral Hospital during August to September of 2015, respectively. CONCLUSION Empirically generated brief procedure code lists based on International Classification of Diseases, 9th Revision, Clinical Modification can be used to classify almost all surgical procedures performed at a Ugandan referral hospital. Such a standardized procedure coding system may enable better surgical data collection for administration, research, and quality improvement in resource-limited settings.
Collapse
Affiliation(s)
- Charles Liu
- Stanford University Medical Center, Stanford, CA.
| | - Peter Kayima
- Mbarara University of Science and Technology, Mbarara Regional Referral Hospital, Mbarara, Uganda
| | - Johanna Riesel
- Harvard Combined Plastic Surgery Residency, Brigham and Women's Hospital, Boston, MA
| | - Martin Situma
- Mbarara University of Science and Technology, Mbarara Regional Referral Hospital, Mbarara, Uganda
| | | | - Paul Firth
- Massachusetts General Hospital, Boston, MA
| |
Collapse
|
17
|
|
18
|
Abstract
BACKGROUND Risk adjustment is an important component of surgical outcomes and quality analyses. Current models include numerous preoperative variables; however, the relative contribution of these variables may be limited. This research seeks to identify a model with the fewest number of variables necessary to perform an adequate risk adjustment to predict any inpatient adverse event for use in resource-limited settings. METHODS All patients from the National Surgical Quality Improvement Program (NSQIP) database from 2005 to 2010 were included. Outcomes were inpatient mortality or any surgical complication captured by NSQIP. Models were built by sequential addition of preoperative risk variables selected by their area under the receiver operator characteristic curve (AUC). RESULTS Among 863,349 patients, the single variable with the highest AUC was American Society of Anesthesiologists (ASA) classification (AUC = 0.7127). AUC values reached 0.7923 with five variables (ASA classification, wound classification, functional status prior to surgery, albumin, and age) and 0.7945 with six variables. The sixth variable was one of the following: alkaline phosphatase, weight loss, principal anesthesia technique, gender, or emergency status. The model with the highest discrimination that did not require laboratories included ASA classification, functional status prior to surgery, wound classification, and age (AUC = 0.7810). Including all 66 preoperative variables produced little additional gain (AUC = 0.8006). CONCLUSIONS Six variables are sufficient to develop a risk adjustment tool for inpatient surgical mortality and morbidity. This research has important implications for the field of surgical outcomes research by improving efficiency of data collection. This limited model can aid the expansion of risk-adjusted analyses to resource-limited settings worldwide.
Collapse
|
19
|
Bosanquet DC, Twine CP, Tang TY, Boyle JR, Bell RE, Bicknell CD, Jenkins MP, Loftus IM, Modarai B, Vallabhaneni SR. Pragmatic Minimum Reporting Standards for Thoracic Endovascular Aortic Repair. J Endovasc Ther 2015; 22:356-67. [DOI: 10.1177/1526602815584925] [Citation(s) in RCA: 8] [Impact Index Per Article: 0.9] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Affiliation(s)
- David C. Bosanquet
- South East Wales Regional Vascular Network, Royal Gwent Hospital, Newport, Wales, UK
| | - Christopher P. Twine
- South East Wales Regional Vascular Network, Royal Gwent Hospital, Newport, Wales, UK
| | - Tjun Y. Tang
- Department of General Surgery, Changi General Hospital, Singapore
| | - Jonathan R. Boyle
- Regional Vascular Unit, Cambridge University Hospital NHS Foundation Trust, Cambridge, UK
| | | | | | | | | | | | | | | |
Collapse
|
20
|
Rose J, Bendix P, Funzamo C, Vaz F, da Costa AA, Bickler S, Noormahomed EV. Universities form research partnership to improve care in Mozambique. BULLETIN OF THE AMERICAN COLLEGE OF SURGEONS 2015; 100:27-34. [PMID: 25626263 PMCID: PMC5454477] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Subscribe] [Scholar Register] [Indexed: 06/04/2023]
Abstract
Mozambique, a country in sub-Saharan Africa, has experienced a severe physician shortage for many years, with patients having particularly limited access to surgical care. Medical education and surgical research partnerships formed in 2010 between Universidade Eduardo Mondlane (UEM) in Mozambique and the University of California, San Diego (UCSD), is developing potential solutions to the nation’s surgical care crisis. The findings that have emerged from this initiative, which are aimed at scaling up surgical services, will likely have important implications for improving surgical care in Mozambique and other underserved nations.
Collapse
|
21
|
Inter-Rater Reliability of the PIPES Tool: Validation of a Surgical Capacity Index for Use in Resource-Limited Settings. World J Surg 2014; 38:2195-9. [DOI: 10.1007/s00268-014-2522-2] [Citation(s) in RCA: 14] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/25/2022]
|
22
|
Macro vs Micro Level Surgical Quality Improvement: A Regional Collaborative Demonstrates the Case for a National NSQIP Initiative. J Am Coll Surg 2014; 218:599-604. [DOI: 10.1016/j.jamcollsurg.2013.12.017] [Citation(s) in RCA: 12] [Impact Index Per Article: 1.2] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2013] [Accepted: 12/10/2013] [Indexed: 11/20/2022]
|
23
|
Decker MR, Greenblatt DY. Optimizing risk-adjusted outcome measures: a moving target. Invited commentary on: Variability of NSQIP assessed surgical quality based on age and disease process. J Surg Res 2013; 186:109-11. [PMID: 23453835 DOI: 10.1016/j.jss.2013.01.060] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.1] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2013] [Revised: 01/17/2013] [Accepted: 01/30/2013] [Indexed: 11/28/2022]
Affiliation(s)
- Marquita R Decker
- Wisconsin Surgical Outcomes Research (WiSOR) Program; Department of Surgery, University of Wisconsin, Madison, Wisconsin.
| | | |
Collapse
|