JRMO Non-CTIMP Protocol Template



Chronic disease within the NHS surgical population:An epidemiological study statistical analysis planAlexander J Fowler1,2, John Prowle1, RM Pearse1, David Cromwell2Queen Mary University of London, London, UKRoyal College of Surgeons of England, London, UKIntroductionMore than 5 million surgical procedures are performed in the National Health Service (NHS) each year, with 49,000 deaths within 30 days.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/bja/aex137","ISSN":"0007-0912","abstract":"Background: Despite evidence of high activity, the number of surgical procedures performed in UK hospitals, their cost and subsequent mortality remain unclear.Methods: Time-trend ecological study using hospital episode data from England, Scotland, Wales and Northern Ireland. The primary outcome was the number of in-hospital procedures, grouped using three increasingly specific categories of surgery. Secondary outcomes were all-cause mortality, length of hospital stay and healthcare costs according to standard National Health Service tariffs.Results: Between April 1, 2009 and March 31, 2014, 39 631 801 surgical patient episodes were recorded. There was an annual average of 7 926 360 procedures (inclusive category), 5 104 165 procedures (intermediate category) and 1 526 421 procedures (restrictive category). This equates to 12 537, 8073 and 2414 procedures per 100 000 population per year, respectively. On average there were 85 181 deaths (1.1%) within 30 days of a procedure each year, rising to 178 040 deaths (2.3%) after 90 days. Approximately 62.8% of all procedures were day cases. Median length of stay for in-patient procedures was 1.7 (1.3–2.0) days. The total cost of surgery over the 5 yr period was ?54.6 billion ($104.4 billion), representing an average annual cost of ?10.9 billion (inclusive), ?9.5 billion (intermediate) and ?5.6 billion (restrictive). For each category, the number of procedures increased each year, while mortality decreased. One-third of all mortalities in national death registers occurred within 90 days of a procedure (inclusive category).Conclusions: The number of surgical procedures in the UK varies widely according to definition. The number of procedures is slowly increasing whilst the number of deaths is decreasing.","author":[{"dropping-particle":"","family":"Abbott","given":"T. E. F.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fowler","given":"A. J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dobbs","given":"T. D.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Harrison","given":"E. M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gillies","given":"M. A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pearse","given":"R. M.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"British Journal of Anaesthesia","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2017"]]},"page":"249-257","title":"Frequency of surgical treatment and related hospital procedures in the UK: a national ecological study using hospital episode statistics","type":"article-journal","volume":"119"},"uris":[""]}],"mendeley":{"formattedCitation":"¹","plainTextFormattedCitation":"1","previouslyFormattedCitation":"¹"},"properties":{"noteIndex":0},"schema":""}1 Some specialities, such as cardiac, colorectal and vascular surgery collect detailed data describing their patients but these audits leave most surgical activity unaccounted for.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1136/hrt.2006.106393","ISSN":"1468-201X","PMID":"17237128","abstract":"To study changes in coronary artery surgery practice in the years spanning publication of cardiac surgery mortality data in the UK.","author":[{"dropping-particle":"","family":"Bridgewater","given":"Ben","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Grayson","given":"Antony D","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Brooks","given":"Nicholas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Grotte","given":"Geir","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fabri","given":"Brian M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Au","given":"John","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hooper","given":"Tim","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Jones","given":"Mark","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Keogh","given":"Bruce","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Heart (British Cardiac Society)","id":"ITEM-1","issue":"6","issued":{"date-parts":[["2007","6"]]},"page":"744-8","title":"Has the publication of cardiac surgery outcome data been associated with changes in practice in northwest England: an analysis of 25,730 patients undergoing CABG surgery under 30 surgeons over eight years.","type":"article-journal","volume":"93"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1039/c1dt90165f","ISBN":"9788193079508","ISSN":"1477-9226","PMID":"22025029","abstract":"The Royal College of Surgeons of England is an independent professional body committed to enabling surgeons to achieve and maintain the highest standards of surgical practice and patient care. As part of this it supports Audit and the evaluation of clinical effectiveness for surgery. The RCS managed the publication of the 2016 Annual report. The Vascular Society of Great Britain and Ireland is the specialist society that represents vascular surgeons. It is one of the key partners leading the audit. Commissioned By HQIP is led by a consortium of the Academy of Medical Royal Colleges, the Royal College of Nursing and National Voices. Its aim is to promote quality improvement, and in particular to increase the impact that clinical audit has on healthcare quality in England and Wales. HQIP holds the contract to manage and develop the NCA Programme, comprising more than 30 clinical audits that cover care provided to people with a wide range of medical, surgical and mental health conditions.","author":[{"dropping-particle":"","family":"VSQIP","given":"","non-dropping-particle":"","parse-names":false,"suffix":""}],"id":"ITEM-2","issue":"November","issued":{"date-parts":[["2016"]]},"title":"NATIONAL VASCULAR REGISTRY 2016 Annual Report","type":"article-journal"},"uris":[""]}],"mendeley":{"formattedCitation":"^{2 3}","manualFormatting":"2,3","plainTextFormattedCitation":"2 3","previouslyFormattedCitation":"^{2 3}"},"properties":{"noteIndex":0},"schema":""}2,3 A high-risk sub-group of around 250,000 patients are concealed within this large surgical population, and account for four out of five deaths after surgery.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1186/cc4928","ISSN":"1466-609X","PMID":"16749940","abstract":"INTRODUCTION: Little is known about mortality rates following general surgical procedures in the United Kingdom. Deaths are most common in the 'high-risk' surgical population consisting mainly of older patients, with coexisting medical disease, who undergo major surgery. Only limited data are presently available to describe this population. The aim of the present study was to estimate the size of the high-risk general surgical population and to describe the outcome and intensive care unit (ICU) resource use.\n\nMETHODS: Data on inpatient general surgical procedures and ICU admissions in 94 National Health Service hospitals between January 1999 and October 2004 were extracted from the Intensive Care National Audit & Research Centre database and the CHKS database. High-risk surgical procedures were defined prospectively as those for which the mortality rate was 5% or greater.\n\nRESULTS: There were 4,117,727 surgical procedures; 2,893,432 were elective (12,704 deaths; 0.44%) and 1,224,295 were emergencies (65,674 deaths; 5.4%). A high-risk population of 513,924 patients was identified (63,340 deaths; 12.3%), which accounted for 83.8% of deaths but for only 12.5% of procedures. This population had a prolonged hospital stay (median, 16 days; interquartile range, 9-29 days). There were 59,424 ICU admissions (11,398 deaths; 19%). Among admissions directly to the ICU following surgery, there were 31,633 elective admissions with 3,199 deaths (10.1%) and 24,764 emergency admissions with 7,084 deaths (28.6%). The ICU stays were short (median, 1.6 days; interquartile range, 0.8-3.7 days) but hospital admissions for those admitted to the ICU were prolonged (median, 16 days; interquartile range, 10-30 days). Among the ICU population, 40.8% of deaths occurred after the initial discharge from the ICU. The highest mortality rate (39%) occurred in the population admitted to the ICU following initial postoperative care on a standard ward.\n\nCONCLUSION: A large high-risk surgical population accounts for 12.5% of surgical procedures but for more than 80% of deaths. Despite high mortality rates, fewer than 15% of these patients are admitted to the ICU.","author":[{"dropping-particle":"","family":"Pearse","given":"Rupert M","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Harrison","given":"David a","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"James","given":"Philip","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Watson","given":"David","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hinds","given":"Charles","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Rhodes","given":"Andrew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Grounds","given":"R Michael","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bennett","given":"E David","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Critical care (London, England)","id":"ITEM-1","issue":"3","issued":{"date-parts":[["2006","1"]]},"page":"R81","title":"Identification and characterisation of the high-risk surgical population in the United Kingdom.","type":"article-journal","volume":"10"},"uris":[""]}],"mendeley":{"formattedCitation":"⁴","plainTextFormattedCitation":"4","previouslyFormattedCitation":"⁴"},"properties":{"noteIndex":0},"schema":""}4 These patients are thought to be at increased risk by virtue of existing chronic diseases, advanced age and the magnitude of the required surgery. The age of the surgical population is increasing more rapidly than the age of the general population.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1002/bjs.11148","ISSN":"1365-2168 (Electronic)","PMID":"31115918","abstract":"BACKGROUND: Advancing age is independently associated with poor postoperative outcomes. The ageing of the general population is a major concern for healthcare providers. Trends in age were studied among patients undergoing surgery in the National Health Service in England. METHODS: Time trend ecological analysis was undertaken of Hospital Episode Statistics and Office for National Statistics data for England from 1999 to 2015. The proportion of patients undergoing surgery in different age groupings, their pooled mean age, and change in age profile over time were calculated. Growth in the surgical population was estimated, with associated costs, to the year 2030 by use of linear regression modelling. RESULTS: Some 68 205 695 surgical patient episodes (31 220 341 men, 45.8 per cent) were identified. The mean duration of hospital stay was 5.3 days. The surgical population was older than the general population of England; this gap increased over time (1999: 47.5 versus 38.3 years; 2015: 54.2 versus 39.7 years). The number of people aged 75 years or more undergoing surgery increased from 544 998 (14.9 per cent of that age group) in 1999 to 1 012 517 (22.9 per cent) in 2015. By 2030, it is estimated that one-fifth of the 75 years and older age category will undergo surgery each year (1.49 (95 per cent c.i. 1.43 to 1.55) million people), at a cost of euro3.2 (3.1 to 3.5) billion. CONCLUSION: The population having surgery in England is ageing at a faster rate than the general population. Healthcare policies must adapt to ensure that provision of surgical treatments remains safe and sustainable.","author":[{"dropping-particle":"","family":"Fowler","given":"A J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Abbott","given":"T E F","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Prowle","given":"J","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pearse","given":"R M","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The British journal of surgery","id":"ITEM-1","issue":"8","issued":{"date-parts":[["2019","7"]]},"language":"eng","page":"1012-1018","publisher-place":"England","title":"Age of patients undergoing surgery.","type":"article-journal","volume":"106"},"uris":[""]}],"mendeley":{"formattedCitation":"⁵","plainTextFormattedCitation":"5","previouslyFormattedCitation":"⁵"},"properties":{"noteIndex":0},"schema":""}5 Advancing age is associated with the development of chronic diseases such as heart failure, chronic kidney disease and dementia.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1016/S2589-7500(19)30012-3","ISSN":"2589-7500 (Electronic)","PMID":"31650125","abstract":"Background: To effectively prevent, detect, and treat health conditions that affect people during their lifecourse, health-care professionals and researchers need to know which sections of the population are susceptible to which health conditions and at which ages. Hence, we aimed to map the course of human health by identifying the 50 most common health conditions in each decade of life and estimating the median age at first diagnosis. Methods: We developed phenotyping algorithms and codelists for physical and mental health conditions that involve intensive use of health-care resources. Individuals older than 1 year were included in the study if their primary-care and hospital-admission records met research standards set by the Clinical Practice Research Datalink and they had been registered in a general practice in England contributing up-to-standard data for at least 1 year during the study period. We used linked records of individuals from the CALIBER platform to calculate the sex-standardised cumulative incidence for these conditions by 10-year age groups between April 1, 2010, and March 31, 2015. We also derived the median age at diagnosis and prevalence estimates stratified by age, sex, and ethnicity (black, white, south Asian) over the study period from the primary-care and secondary-care records of patients. Findings: We developed case definitions for 308 disease phenotypes. We used records of 2 784 138 patients for the calculation of cumulative incidence and of 3 872 451 patients for the calculation of period prevalence and median age at diagnosis of these conditions. Conditions that first gained prominence at key stages of life were: atopic conditions and infections that led to hospital admission in children (<10 years); acne and menstrual disorders in the teenage years (10-19 years); mental health conditions, obesity, and migraine in individuals aged 20-29 years; soft-tissue disorders and gastro-oesophageal reflux disease in individuals aged 30-39 years; dyslipidaemia, hypertension, and erectile dysfunction in individuals aged 40-59 years; cancer, osteoarthritis, benign prostatic hyperplasia, cataract, diverticular disease, type 2 diabetes, and deafness in individuals aged 60-79 years; and atrial fibrillation, dementia, acute and chronic kidney disease, heart failure, ischaemic heart disease, anaemia, and osteoporosis in individuals aged 80 years or older. Black or south-Asian individuals were diagnosed earlier than white individuals for 258…","author":[{"dropping-particle":"","family":"Kuan","given":"Valerie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Denaxas","given":"Spiros","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gonzalez-Izquierdo","given":"Arturo","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Direk","given":"Kenan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Bhatti","given":"Osman","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Husain","given":"Shanaz","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sutaria","given":"Shailen","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hingorani","given":"Melanie","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Nitsch","given":"Dorothea","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Parisinos","given":"Constantinos A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Lumbers","given":"R Thomas","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Mathur","given":"Rohini","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sofat","given":"Reecha","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Casas","given":"Juan P","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Wong","given":"Ian C K","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hemingway","given":"Harry","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Hingorani","given":"Aroon D","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"The Lancet. Digital health","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2019","6"]]},"language":"eng","page":"e63-e77","publisher-place":"England","title":"A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service.","type":"article-journal","volume":"1"},"uris":["",""]}],"mendeley":{"formattedCitation":"⁶","plainTextFormattedCitation":"6","previouslyFormattedCitation":"⁶"},"properties":{"noteIndex":0},"schema":""}6 While certain chronic diseases are associated with reduced survival after surgical procedures, the detailed association between chronic disease and outcomes are unclear in a broad surgical cohort. Existing aggregate scores such as the Charlson co-morbidity index may attribute similar scores to different conditions with very different survival patterns.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1002/bjs.6930","ISSN":"00071323","abstract":"BACKGROUND: Surgical outcomes are influenced by co-morbidity. The Royal College of Surgeons (RCS) Co-morbidity Consensus Group was convened to improve existing instruments that identify co-morbidity in International Classification of Diseases tenth revision administrative data. METHODS: The RCS Charlson Score was developed using a coding philosophy that enhances international transferability and avoids misclassifying complications as co-morbidity. The score was validated in English Hospital Episode Statistics data for abdominal aortic aneurysm (AAA) repair, aortic valve replacement, total hip replacement and transurethral prostate resection. RESULTS: With exception of AAA, patients with co-morbidity were older and more likely to be admitted as an emergency than those without. All patients with co-morbidity stayed longer in hospital, required more augmented care, and had higher in-hospital and 1-year mortality rates. Multivariable prognostic models incorporating the RCS Charlson Score had better discriminatory power than those that relied only on age, sex, admission method (elective or emergency) and number of emergency admissions in the preceding year. CONCLUSION: The RCS Charlson Score identifies co-morbidity in surgical patients in England at least as well as existing instruments. Given its explicit coding philosophy, it may be used as a co-morbidity scoring instrument for international comparisons.","author":[{"dropping-particle":"","family":"Armitage","given":"J. N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Meulen","given":"J. H.","non-dropping-particle":"Van Der","parse-names":false,"suffix":""}],"container-title":"British Journal of Surgery","id":"ITEM-1","issue":"5","issued":{"date-parts":[["2010"]]},"page":"772-781","title":"Identifying co-morbidity in surgical patients using administrative data with the Royal College of Surgeons Charlson Score","type":"article-journal","volume":"97"},"uris":[""]}],"mendeley":{"formattedCitation":"⁷","plainTextFormattedCitation":"7","previouslyFormattedCitation":"⁷"},"properties":{"noteIndex":0},"schema":""}7 For example, both metastatic solid cancers and rheumatological disorders score one point but metastatic disease has far higher risk of death. At present, the prevalence of different chronic diseases amongst patients undergoing surgery, and the association between these diseases and survival after surgery is unclear. The aim of this study will be to describe the chronic diseases most commonly suffered by the surgical population in the England, and the association between these diseases and subsequent outcomes after surgery.Study objectivesHypothesisAmongst patients undergoing surgery in the NHS, different chronic diseases are associated with different rates of death after surgery.Primary objectiveTo describe the rate of 90 day death associated with different chronic diseases amongst patients undergoing surgerySecondary objectivesTo determine the most frequent chronic diseases suffered by patients undergoing surgeryTo describe the change in prevalence of different chronic diseases with ageTo describe other relevant outcomes amongst patients with different chronic diseases including length of hospital stay, death within two years, hospital re-admission and number of days spent in a hospital bed in the year after surgery.Primary outcomeDeath within 90-days of index surgical procedureSecondary outcomesLength of hospital following index surgical procedure, reported in whole daysDeath within two years of index surgical procedureRate of re-admission to hospital within one year of index surgical procedureTotal number of days spent in hospital within one year of index surgical procedure, expressed as a proportion of days aliveStudy populationAll patients undergoing surgery in England between 1st January 2005 – 31st December 2015 in a National Health Service hospital will be considered for inclusion. Data sourcesHospital Episode Statistics, NHS England (2005-2016)Office for National Statistics death registry data (2005-2018)Definition of surgeryAdult patients who meet a prior definition based on Office of Population Censuses and Surveys intervention and procedure codes (OPCS version 4.7) will be included.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1093/bja/aex137","ISSN":"0007-0912","abstract":"Background: Despite evidence of high activity, the number of surgical procedures performed in UK hospitals, their cost and subsequent mortality remain unclear.Methods: Time-trend ecological study using hospital episode data from England, Scotland, Wales and Northern Ireland. The primary outcome was the number of in-hospital procedures, grouped using three increasingly specific categories of surgery. Secondary outcomes were all-cause mortality, length of hospital stay and healthcare costs according to standard National Health Service tariffs.Results: Between April 1, 2009 and March 31, 2014, 39 631 801 surgical patient episodes were recorded. There was an annual average of 7 926 360 procedures (inclusive category), 5 104 165 procedures (intermediate category) and 1 526 421 procedures (restrictive category). This equates to 12 537, 8073 and 2414 procedures per 100 000 population per year, respectively. On average there were 85 181 deaths (1.1%) within 30 days of a procedure each year, rising to 178 040 deaths (2.3%) after 90 days. Approximately 62.8% of all procedures were day cases. Median length of stay for in-patient procedures was 1.7 (1.3–2.0) days. The total cost of surgery over the 5 yr period was ?54.6 billion ($104.4 billion), representing an average annual cost of ?10.9 billion (inclusive), ?9.5 billion (intermediate) and ?5.6 billion (restrictive). For each category, the number of procedures increased each year, while mortality decreased. One-third of all mortalities in national death registers occurred within 90 days of a procedure (inclusive category).Conclusions: The number of surgical procedures in the UK varies widely according to definition. The number of procedures is slowly increasing whilst the number of deaths is decreasing.","author":[{"dropping-particle":"","family":"Abbott","given":"T. E. F.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fowler","given":"A. J.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Dobbs","given":"T. D.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Harrison","given":"E. M.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Gillies","given":"M. A.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Pearse","given":"R. M.","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"British Journal of Anaesthesia","id":"ITEM-1","issue":"2","issued":{"date-parts":[["2017"]]},"page":"249-257","title":"Frequency of surgical treatment and related hospital procedures in the UK: a national ecological study using hospital episode statistics","type":"article-journal","volume":"119"},"uris":[""]}],"mendeley":{"formattedCitation":"¹","plainTextFormattedCitation":"1","previouslyFormattedCitation":"¹"},"properties":{"noteIndex":0},"schema":""}1 This definition selects procedures typically performed in an operating theatre, or requiring general/regional anaesthesia. This definition of surgery was developed using three-character OPCS codes (e.g. L91; Other vein related operations).Each three-character OPCS code has a further layer of information with a fourth character (e.g. L91.1; Open insertion of central venous catheter). Administrative data sets are coded according to the four-character OPCS codes. The two hundred commonest procedures performed will be reviewed by two researchers acting independently to ensure all four-character codes are relevant. The initial search will include all patients meeting the three-character definition and will be further refined to the four-character definition. Any codes excluded will be listed in a supplementary appendix.Inclusion criteriaPatients aged greater than 18 years on date of admission, where the admission date was between 1st January 2005 and 1st January 2015 will be considered for inclusion. Patients will be included if they undergo a procedure meeting the refined definition of surgery. A flow of patient selection steps is outlined in figure 1. Where patients have multiple procedures over the period studied, only their first will be included.Exclusion criteriaAll organ retrieval operations will be excluded from analysis (identified by OPCS version 4.7: X45, X46). VariablesThe admission category will be defined by a combination of patient class (inpatient or day) and type of admission (emergency, elective) into three groups: elective inpatient, elective day and emergency inpatient. Age at time of surgery will be defined as the age in whole years at the start of the index hospital admission. Procedure grouping will be based on anatomical location of index procedure, divided into 21 categories. A two-year look back file will capture diagnostic codes relevant to chronic diseases. Mappings have been developed to combine multiple International Classification of Disease version 10 codes ICD-10 codes) into disease specific domains.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1002/bjs.6930","ISSN":"00071323","abstract":"BACKGROUND: Surgical outcomes are influenced by co-morbidity. The Royal College of Surgeons (RCS) Co-morbidity Consensus Group was convened to improve existing instruments that identify co-morbidity in International Classification of Diseases tenth revision administrative data. METHODS: The RCS Charlson Score was developed using a coding philosophy that enhances international transferability and avoids misclassifying complications as co-morbidity. The score was validated in English Hospital Episode Statistics data for abdominal aortic aneurysm (AAA) repair, aortic valve replacement, total hip replacement and transurethral prostate resection. RESULTS: With exception of AAA, patients with co-morbidity were older and more likely to be admitted as an emergency than those without. All patients with co-morbidity stayed longer in hospital, required more augmented care, and had higher in-hospital and 1-year mortality rates. Multivariable prognostic models incorporating the RCS Charlson Score had better discriminatory power than those that relied only on age, sex, admission method (elective or emergency) and number of emergency admissions in the preceding year. CONCLUSION: The RCS Charlson Score identifies co-morbidity in surgical patients in England at least as well as existing instruments. Given its explicit coding philosophy, it may be used as a co-morbidity scoring instrument for international comparisons.","author":[{"dropping-particle":"","family":"Armitage","given":"J. N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Meulen","given":"J. H.","non-dropping-particle":"Van Der","parse-names":false,"suffix":""}],"container-title":"British Journal of Surgery","id":"ITEM-1","issue":"5","issued":{"date-parts":[["2010"]]},"page":"772-781","title":"Identifying co-morbidity in surgical patients using administrative data with the Royal College of Surgeons Charlson Score","type":"article-journal","volume":"97"},"uris":[""]},{"id":"ITEM-2","itemData":{"DOI":"10.1097/01.mlr.0000182534.19832.83","ISSN":"0025-7079 (Print)","PMID":"16224307","abstract":"OBJECTIVES: Implementation of the International Statistical Classification of Disease and Related Health Problems, 10th Revision (ICD-10) coding system presents challenges for using administrative data. Recognizing this, we conducted a multistep process to develop ICD-10 coding algorithms to define Charlson and Elixhauser comorbidities in administrative data and assess the performance of the resulting algorithms. METHODS: ICD-10 coding algorithms were developed by \"translation\" of the ICD-9-CM codes constituting Deyo's (for Charlson comorbidities) and Elixhauser's coding algorithms and by physicians' assessment of the face-validity of selected ICD-10 codes. The process of carefully developing ICD-10 algorithms also produced modified and enhanced ICD-9-CM coding algorithms for the Charlson and Elixhauser comorbidities. We then used data on in-patients aged 18 years and older in ICD-9-CM and ICD-10 administrative hospital discharge data from a Canadian health region to assess the comorbidity frequencies and mortality prediction achieved by the original ICD-9-CM algorithms, the enhanced ICD-9-CM algorithms, and the new ICD-10 coding algorithms. RESULTS: Among 56,585 patients in the ICD-9-CM data and 58,805 patients in the ICD-10 data, frequencies of the 17 Charlson comorbidities and the 30 Elixhauser comorbidities remained generally similar across algorithms. The new ICD-10 and enhanced ICD-9-CM coding algorithms either matched or outperformed the original Deyo and Elixhauser ICD-9-CM coding algorithms in predicting in-hospital mortality. The C-statistic was 0.842 for Deyo's ICD-9-CM coding algorithm, 0.860 for the ICD-10 coding algorithm, and 0.859 for the enhanced ICD-9-CM coding algorithm, 0.868 for the original Elixhauser ICD-9-CM coding algorithm, 0.870 for the ICD-10 coding algorithm and 0.878 for the enhanced ICD-9-CM coding algorithm. CONCLUSIONS: These newly developed ICD-10 and ICD-9-CM comorbidity coding algorithms produce similar estimates of comorbidity prevalence in administrative data, and may outperform existing ICD-9-CM coding algorithms.","author":[{"dropping-particle":"","family":"Quan","given":"Hude","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Sundararajan","given":"Vijaya","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Halfon","given":"Patricia","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Fong","given":"Andrew","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Burnand","given":"Bernard","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Luthi","given":"Jean-Christophe","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Saunders","given":"L Duncan","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Beck","given":"Cynthia A","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Feasby","given":"Thomas E","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Ghali","given":"William A","non-dropping-particle":"","parse-names":false,"suffix":""}],"container-title":"Medical care","id":"ITEM-2","issue":"11","issued":{"date-parts":[["2005","11"]]},"language":"eng","page":"1130-1139","publisher-place":"United States","title":"Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data.","type":"article-journal","volume":"43"},"uris":["",""]}],"mendeley":{"formattedCitation":"^{7 8}","manualFormatting":"7,9","plainTextFormattedCitation":"7 8","previouslyFormattedCitation":"^{7 8}"},"properties":{"noteIndex":0},"schema":""}7,9 ICD-10 codes will be mapped to Charlson co-morbidity index domains (‘Charlson domains’) according to the Royal College of Surgeons Charlson mapping.ADDIN CSL_CITATION {"citationItems":[{"id":"ITEM-1","itemData":{"DOI":"10.1002/bjs.6930","ISSN":"00071323","abstract":"BACKGROUND: Surgical outcomes are influenced by co-morbidity. The Royal College of Surgeons (RCS) Co-morbidity Consensus Group was convened to improve existing instruments that identify co-morbidity in International Classification of Diseases tenth revision administrative data. METHODS: The RCS Charlson Score was developed using a coding philosophy that enhances international transferability and avoids misclassifying complications as co-morbidity. The score was validated in English Hospital Episode Statistics data for abdominal aortic aneurysm (AAA) repair, aortic valve replacement, total hip replacement and transurethral prostate resection. RESULTS: With exception of AAA, patients with co-morbidity were older and more likely to be admitted as an emergency than those without. All patients with co-morbidity stayed longer in hospital, required more augmented care, and had higher in-hospital and 1-year mortality rates. Multivariable prognostic models incorporating the RCS Charlson Score had better discriminatory power than those that relied only on age, sex, admission method (elective or emergency) and number of emergency admissions in the preceding year. CONCLUSION: The RCS Charlson Score identifies co-morbidity in surgical patients in England at least as well as existing instruments. Given its explicit coding philosophy, it may be used as a co-morbidity scoring instrument for international comparisons.","author":[{"dropping-particle":"","family":"Armitage","given":"J. N.","non-dropping-particle":"","parse-names":false,"suffix":""},{"dropping-particle":"","family":"Meulen","given":"J. H.","non-dropping-particle":"Van Der","parse-names":false,"suffix":""}],"container-title":"British Journal of Surgery","id":"ITEM-1","issue":"5","issued":{"date-parts":[["2010"]]},"page":"772-781","title":"Identifying co-morbidity in surgical patients using administrative data with the Royal College of Surgeons Charlson Score","type":"article-journal","volume":"97"},"uris":[""]}],"mendeley":{"formattedCitation":"⁷","plainTextFormattedCitation":"7","previouslyFormattedCitation":"⁷"},"properties":{"noteIndex":0},"schema":""}7 This mapping was developed specifically to describe co-morbidity burden amongst surgical patients in the NHS. In this analysis, the Charlson co-morbidity index score (CCI) will be calculated as the sum of all fourteen Charlson domains. Where the CCI is greater than or equal to six, the score will be normalised to six. A look-forward file will capture subsequent hospital re-admission within one year. A patient will be considered re-admitted to hospital if they have a record within the admitted patient care data set with a length of stay >0 days. Bed days will be calculated by summing the number of days spent as an inpatient in an NHS hospital in England in the year after surgery. To account for the competing endpoint of death, the number of bed days will be expressed as a proportion of days alive. For example if someone dies within 30 days of surgery and spends 15 days of those in hospital, their bed days proportion would be 0.5. If someone dies after 3 days and spends all those days in hospital, their proportion would be 1.Deprivation will be determined by patient level index of multiple deprivation 2010 rank (IMD2010). This is based on the lower super-output area (LSOA) in which an individual resides, each LSOA describes a geographic area that includes around 1500 homes. IMD2010 combines a number of different social deprivation measures to rank each LSOA from the most deprived to the least deprived across England. Patients will be divided according to the IMD2010 rank of their LSOA into quintiles from 1 (most deprived quintile) to 5 (least deprived quintile).Statistical considerationsSample sizeNo sample size analysis has been performed. The number of patients meeting the above inclusion criteria will determine the sample size of this prospectively designed analysis of registry data study. Based on prior work, it is anticipated around ~30 million unique patients will be included.Missing data Where data are missing for patient demographic information (e.g. age or gender), process information (e.g. type of admission and patient category), or outcome variables (e.g. length of stay) and the proportion of cases with missing data is <1%, then records will be removed by case-wise deletion. A supplementary table will be provided summarising the characteristics of removed records. Where the rate of missing data exceeds 1%, the pattern of the missing-ness will be assessed, and multiple imputation with chained equations considered. Ethnicity is poorly recorded in HES, with around 10% of records having a missing ethnicity code. Missing ethnicity will be handled as its own category. Records with implausible outcomes, such as a negative length of stay, will be manually reviewed for removal. All analyses will be completed using R (version 3.6.1, R Core Team, Vienna).Patient characteristicsCharacteristics of patients in each admission category (elective inpatient, elective day-case, emergency inpatient) will be presented. This will include the mean (with standard deviation) and median (with interquartile range) of age, frequency of different sexes, CCI, distribution of deprivation in quintiles, and the most commonly performed procedures (as per example table 1). Frequency of chronic diseasesTo describe the frequency of different chronic diseases amongst patients undergoing surgery, the number of patients suffering from each disease will be presented (example table 2), subdivided by admission category. Change in chronic disease frequency with ageTo determine how chronic disease frequency alters with age in the surgical population, the proportion of patients suffering each disease at each age will be presented as in example figure 2. A supplementary table will include the number of patients of each age with each disease.Association between chronic diseases and outcomesTo identify the association between different chronic diseases and patient relevant outcomes, a table will be presented outlining the number of patients dying within 90-days, 2-years, length of stay, the rate of hospital re-admission and bed days spent in hospital in the year after surgery, as in example table 3. To adjust for the effect of age on outcomes for individual chronic diseases, a univariate analysis will be performed adding age as an interaction term and an odds ratio for death will be presented as in table 3. To demonstrate how the association between different chronic diseases interacts with age, the rate of 90-day death will be plotted against age with a line for each chronic disease (as in example figure 3). A Kaplan-Meier survival curve will be plotted for each chronic disease for survival out to two years. Prototypical proceduresAbove analyses will include all surgical patients. To explore if the association between different chronic diseases and subsequent outcomes persist in common surgical settings, the relationship between each chronic disease and outcomes will be presented as in example table 3 for specific procedures. Each row of the table will be divided into one of four surgical procedures (codes defining these procedures are present in Appendix B):Cataract surgery (very low risk procedure)Total hip replacement (low risk procedure)Coronary artery bypass graft (moderate risk procedure)Colorectal cancer resection (high risk procedure)Risk is based on aggregated 90-day mortality for the above procedures.References ADDIN Mendeley Bibliography CSL_BIBLIOGRAPHY 1. Abbott TEF, Fowler AJ, Dobbs TD, Harrison EM, Gillies MA, Pearse RM. Frequency of surgical treatment and related hospital procedures in the UK: a national ecological study using hospital episode statistics. Br J Anaesth 2017; 119: 249–57 2. Bridgewater B, Grayson AD, Brooks N, et al. Has the publication of cardiac surgery outcome data been associated with changes in practice in northwest England: an analysis of 25,730 patients undergoing CABG surgery under 30 surgeons over eight years. Heart 2007; 93: 744–8 3. VSQIP. NATIONAL VASCULAR REGISTRY 2016 Annual Report. 2016; 4. Pearse RM, Harrison D a, James P, et al. Identification and characterisation of the high-risk surgical population in the United Kingdom. Crit Care 2006; 10: R81 5. Fowler AJ, Abbott TEF, Prowle J, Pearse RM. Age of patients undergoing surgery. Br J Surg England; 2019; 106: 1012–8 6. Kuan V, Denaxas S, Gonzalez-Izquierdo A, et al. A chronological map of 308 physical and mental health conditions from 4 million individuals in the English National Health Service. Lancet Digit Heal England; 2019; 1: e63–77 7. Armitage JN, Van Der Meulen JH. Identifying co-morbidity in surgical patients using administrative data with the Royal College of Surgeons Charlson Score. Br J Surg 2010; 97: 772–81 8. Quan H, Sundararajan V, Halfon P, et al. Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data. Med Care United States; 2005; 43: 1130–9 9. Brandes U, Delling D, Gaertler M, et al. On Modularity Clustering. IEEE Trans Knowl Data Eng 2008; 20: 172–88 Sample tables and figuresElective inpatientElective daycaseEmergencyAgeMean (SD)Median (IQR)SexFemaleMaleSpecialtyOrthopaedicsHepatopancreatobiliaryUpper gastrointestinalLower gastrointestinalCardiacThoracicGynaecologyPlasticsBreastEndocrineVascularUrology and kidneyHead and neckOtherChronic diseasesMyocardial infarctionDiabetes mellitusChronic lung diseaseChronic kidney diseaseDementiaCongestive cardiac failurePeripheral vascular diseaseCerebrovascular diseaseMetastatic solid cancerCancerHemi/paraplegiaHIVRheumatologicalStrokeLiver diseaseCCI (median [IQR])Year2005-20102010-2015Table 1a. Characteristics of included patients undergoing surgery subdivided according to different acuity, Charlson co-morbidity index score; SD, standard deviation; IQR, interquartile range.Charlson co-morbidity index domainsElective inpatientElective day caseEmergencyAll patientsMICCFPVDCVDDementiaCOPDLiver disease Rheumatological DiabetesHemi/ParaRenal diseaseMalignancyMetastatic solidHIV Table 2. The frequency of different chronic diseases as captured by the Charlson co-morbidity index across elective inpatient, elective day and emergency surgery. Charlson co-morbidity index mapped according to Royal College of Surgeons Charlson score. Numbers are presented as % (number with diseases/total number of operations). HIV; Human immunodeficiency virus. COPD; Chronic obstructiveCharlson co-morbidity index domains90-day death2-year deathLength of stayBed days ReadmissionOdds ratioMean (sd)Median (iqr)Mean (sd)Median (iqr)MICCFPVDCVDDementiaCOPDLiver disease Rheumatological DiabetesHemi/ParaRenal diseaseMalignancyMetastatic solidHIV Example table 3. Outcomes associated with different chronic disease conditions as captured by the Charlson co-morbidity index. Charlson co-morbidity index mapped according to Royal College of Surgeons Charlson score. HIV; Human immunodeficiency virus. COPD; Chronic obstructive pulmonary disease. sd; standard deviation. iqr; interquartile range. Bed days is the number of days spent in hospital over the year after date of index admission, expressed as a proportion of days spent alive. 90-day death is the total number of deaths within 90 days. Readmission is the count of patients re-admitted at any time point in the year after surgery. Odds ratio for the death at 90 days associated with each chronic disease corrected for age.Example Figure 1. Flow diagram of patient selection. Reasons for exclusion are provided on the right-hand side and final study population includes those undergoing a surgical procedure meeting a previously defined categorisation of surgery. ADMIMETH; Admission method.Example figure 2. Proportion of patients of different ages undergoing surgery with different chronic diseases. Proportion is the number suffering that disease divided by the total number of persons undergoing surgery with that disease.Example figure 3. Rate of death associated with different chronic disease conditions as age increases. Each colour indicates a different disease. Y axis would be proportion dead at 90 days, or perhaps we could do it as ‘proportion of days alive spent in a hospital bed’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oronary artery bypass grafting procedureCodeSaphenous vein graft replacement of coronary arteryK40Allograft replacement of coronary arteryK42Prosthetic replacement of coronary arteryK43Other replacement of coronary arteryK44Connection of thoracic artery to coronary arteryK45Other bypass of coronary arteryK46Repair of coronary arteryK47Other open operations on coronary arteryK48Colorectal cancer resectionCodeExcision of ileumG69Open extirpation of lesion of ileumG70Bypass of ileumG71Other connection of ileumG72Intra-abdominal manipulation of ileumG76Other open operations on ileumG78Other operations on ileumG82Total excision of colon and rectumH04Total excision of colonH05Extended excision of right hemicolonH06Other excision of right hemicolonH07Excision of transverse colonH08Excision of left hemicolonH09Excision of sigmoid colonH10Other excision of colonH11Extirpation of lesion of colonH12Bypass of colonH13Exteriorisation of caecumH14Other exteriorisation of colonH15Incision of colonH16Intra-abdominal manipulation of colonH17Open endoscopic operations on colonH18Other open operations on colonH19Subtotal excision of colonH29Other operations on colonH30Excision of rectumH33Open extirpation of lesion of rectumH34Other operations on rectumH46Excision of anusH47Excision of lesion of anusH48Other operations on bowelH62Excision of bile ductJ27Extirpation of lesion of bile ductJ28Clearance of pelvisX14Major joint replacement procedureCodeTotal prosthetic replacement of hip joint using cementW37Total prosthetic replacement of hip joint not using cementW38Other total prosthetic replacement of hip jointW39Total prosthetic replacement of knee joint using cementW40Total prosthetic replacement of knee joint not using cementW41Total prosthetic replacement of other joint using cementW43Total prosthetic replacement of other joint not using cementW44Other total prosthetic replacement of other jointW45Prosthetic replacement of head of femur using cementW46Prosthetic replacement of head of femur not using cementW47Other prosthetic replacement of head of femurW48Prosthetic replacement of articulation of other bone using cementW52Prosthetic replacement of articulation of other bone not using cementW53Hybrid prosthetic replacement of hip joint using cemented acetabular componentW93Hybrid prosthetic replacement of hip joint using cemented femoral componentW94Hybrid prosthetic replacement of hip joint using cementW95Cataract proceduresProsthesis of lensC75 ................
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