Real-world data on multimorbidity represents an important but underutilised source of evidence for the planning of healthcare services, including prevention, treatments, and health economic modelling.This study aimed to estimate means of multimorbidity and provide associated prevalence rates and frequencies between 199 x 199 chronic conditions and disease groups based on the total adult Danish population and sex, age, and educational attainment. Thus, this study provides an off-the-shelf catalogue for use in treatments and planning by clinicians, decision-makers and researchers.The study population contained all Danish residents above 16 years on 1 January 2013 . The data was based on the linkage of six national registers covering hospital contacts, services in general practice, filled-in out-of-hospital prescriptions, and educational attainments. The health registers were used to identify the 199 chronic conditions based on the ICD-10 classification system.The mean number of chronic conditions (NCC) was 2.2. The mean increased with age, women had a higher mean than men, and there was a social gradient with the mean increasing with lower educational attainment. The mean NCC varied from 3.3–9.8 among all conditions. Across disease groups, the highest mean NCC were found within disease group N , D , K , and H . The highest mean NCC among the 29 common diseases was heart failure, ischemic heart diseases, angina pectoris, stroke, and dementia, with a mean above 6.5. Several prevalent conditions like hypertension, arthritis, chronic lower respiratory diseases, depression, type 2 diabetes, and overweight transcended other conditions regarding the associated prevalence rates. As one of the most frequent, hypertensive diseases were highly associated with arthritis (50.4%), depression (37.4%), type 2 diabetes (75.4%), cancers (49.7%), and being overweight (39.7%)–meaning that 50.4% of people with arthritis, 37.4% of people with depression and so on also had hypertensive diseases. The largest differences in means between individuals with no educational attainment and individuals with high educational attainment were found within disease groups J , Q , and B .The current study provides a nationwide off-the-shelf catalogue of multimorbidity means and real-world associations estimates of 199+ chronic conditions for future clinical treatments and health care systems planning. The findings described are just one example of numerous results and underline that multimorbidity is highly prevalent in the adult Danish population and that it is a vital condition transcending all future medical treatment. The data offer essential information on the multimorbidity burden of disease in future differentiated treatments, healthcare planning, and economic, aetiological, and other research. Patients with multimorbidity, frequently defined as the coexistence of two or more chronic conditions within the same individual , 2, haveThe high prevalence of multimorbidity is particularly challenging for governments worldwide due to healthcare treatment structures. Healthcare systems worldwide are set up mainly for treating patients with single diseases; thus, most disease guidelines in the health system focus on single diseases . This isMultimorbidity is, however, a multifaceted, entangled, challenging subject to analyse. The Charlson Comorbidity Index , or simpFurthermore, for health professionals, raw, real-life, non-statistically reduced estimates are useful to obtain all details of the disease population of interest for either health care planning or clinical treatment. Another related study has, however, reported the prevalence rates of disease combinations but used self-reported conditions and was limited to 17 conditions . IncludiThe current study aimed to estimate basic, descriptive, nationally representative means of multimorbidity and associated prevalence rates and frequencies of 199 x 199 chronic conditions of the total adult Danish population according to sex, age, and educational attainment. As one measure of severity, the mean NCC will enable researchers, health professionals, health economists, and decision-makers to identify, access, and compare the disease burden of the 199 chronic conditions. The correlational prevalence estimates between the 199 x 199 conditions will give real-world, detailed, unbiased, self-report estimates of the concrete multimorbidity for each of the 199 chronic conditions used in treatments and health care planning. Thus, the study provides an off-the-shelf catalogue and a comparative overview of multimorbidity across 199 chronic conditions. To the best of the authors’ knowledge, the current study provides the most comprehensive descriptive estimates of multimorbidity means and correlational prevalence of chronic conditions based on an entire country’s population, a uniform, comparable methodology and an exceptionally high number of chronic conditions.The nationwide study population included 4,555,439 Danish residents aged 16 years or older alive on 1 January 2013. The study population consisted of 49.2% men, and the mean age was 46.7 years. Forty-five per cent were between 16–44 years old, 46% were between 45–74 years old, and 9% were 75 years old or older.In Denmark, there is a long tradition of reporting diseases, treatments, medications, and contact with the healthcare system, in national health registers. The registers were originally intended for data collection by government officials in public administration at the individual level . All regIn the current study, six registers were applied and linked from Statistics Denmark. The National Patient Register (NPR) , the Dan‘…condition had lasted or was expected to last twelve or more months and resulted in functional limitations and/or the need for functional limitations and/or the need for ongoing medical care’ [A ‘chronic condition’ was defined in line with former studies if the al care’ , 67–69. al care’ . The expal care’ . A detaial care’ , 65, 66.Since numerous chronic conditions last longer than the 12 months used in the definition but do not persist for a lifetime, the ‘severity of chronicity’ was categorised into four categories depending on how long the conditions were expected to last :Category I: Stationary to progressive chronic conditions ;Category II: Stationary to diminishing chronic conditions (10 years from register inclusion time to the time of interest);Category III: Diminishing chronic conditions (5 years from register inclusion time to the time of interest); andCategory IV: Borderline chronic conditions (2 years from register inclusion time to the time of interest).Adapted with permission from Hvidberg et al.  , 65.This method was designed to handle a renowned challenge of register-research: if a disease is only identified once, for instance, 5, 10, or 30 years back in time from a specific date, is it then expected that the patient still suffers from the condition? Hence, the expert panel assigned all of the 199 chronic conditions into one of the four categories. The allocation into one of the four categories was based on a medical judgement on how long time the various ICD-10 diagnoses identified as ‘chronic’, with the best possible clinical conviction, would still have the disease from a time of interest. This systematic approach was employed as a proxy for disease severity. An algorithm based on the medical experts’ definitions identified ICD-10 codes and allocated each of the 199 chronic conditions into the four chronicity categories that were utilised for data collection. However, for 35 of the 199 chronic conditions, the medical experts did not expect the ICD-10 diagnosis to be representative alone. Thus, 35 algorithms were developed based on multiple registers comprising medicine, hospital treatments, and services in general practice , 65, 66.Means of chronic conditions and per cent prevalence were calculated for each of the 199 chronic conditions. Means were calculated as the sum of all subjects’ multimorbidity within the disease of interest, divided by the number of subjects within the disease group and elaborating variables of interest. We used the following elaborating variables: sex, age groups  and educational attainment (no education vs higher education). Per cent prevalence was calculated within diseases of elaborating variables as the number of subjects of the elaborating variable of interest, divided by the total subjects of the disease, multiplied by a hundred. Direct standardised means and prevalence estimates were presented and calculated based on the national proportion of sex and age on 1 January 2013, as referenced , 71 weredisease groups referring to the ICD-10 system and described in detail elsewhere [common conditions plus overweight are presented and commented on in the result section. The common conditions comprise the conditions measured in the National Population Health Surveys every fourth year [All conditions were given ranks according to their NCCs, with one indicating the highest NCCs based on the unstandardised means. To provide the reader with an overview of the comprehensive material, 14 lsewhere  and 29 crth year , among oData management and analysis were done using SAS 9.4 from Statistics Denmark’s remote research servers.Declaration and approval to conduct the study were obtained from the Danish Data Protection Agency and the Secretariat for Research Processing Records, Data and Development Support, Region Zealand (REG-142-2021). No informed consent was required. Statistics Denmark anonymized all register-data before the data were made available on their secured server.The NCCs ranged from 0 to 32 conditions with a highly left-skewed distribution for the population . OverallOverall, the mean NCCs in the population was 2.2 –with a mean of 2.4 among women and 2.0 among men. The mean NCCs increased by age, and women had a higher mean of chronic conditions than men, although this gap narrowed with age . We founThe mean NCCs across the 199 chronic conditions and the disease groups range from around 3 to 9, with the main proportion of conditions having a mean between 5 and 7 chronic conditions .Table 1 displays the mean NCC for the 14 disease groups and sex and age. Disease group N  had the highest mean NCCs (mean = 8.8), followed by disease group D , K , and H . Disease group C (cancers), followed by disease group E , G (diseases of the nervous system), I (diseases of the circulatory system), and F , had a mean of NCCs ranging from 4.8–5.4. Finally, disease group L (diseases of the skin and subcutaneous tissue), M , J (diseases of the respiratory system), and Q  had a mean of NCC ranging from 4.0–4.7. Of the 14 disease groups, sex differences were among others found in disease group D , K  and J . For more details about the overall mean NCCs of the 199 chronic conditions and means by sex and age, see Among the 29 most common chronic conditions and overweight, heart disease, stroke, and dementia had more than seven other chronic conditions . FurtherTable 2 shows the 30 x 30 cross-tabulated prevalence rates between the 29 most common chronic conditions and overweight. The prevalence rates indicate how many per cent within the row condition have the condition in the column. The following commentary is delimited to selected, highly prevalent conditions: hypertensive diseases, arthritis, chronic lower respiratory diseases, depression, type 2 diabetes, cancers and overweight. Hypertensive diseases were associated with respiratory allergy (25.3%), arthritis (24.0%) and ischemic heart diseases (22.6%)–meaning that of the persons with hypertensive diseases, 25.3% also had a respiratory allergy, 24.0% had arthritis, and 22.6 had heart disease. Type 2 diabetes (17.2%), arthrosis (17.0%), depression (16.0%), and chronic lower respiratory diseases (15.2%) were also associated with hypertensive diseases. Arthritis was highly associated with hypertensive diseases (50.4%), gonarthrosis (35.4%), and inflammatory polyarthropathies (32.1%). Moreover, the prevalence of respiratory allergies was high among people with arthritis (26.2%). Chronic lower respiratory diseases were highly associated with asthma (57.7%) and respiratory allergy (43.1%). But high prevalence was also found among hypertensive diseases (38.5%), COPD (29.0%), and arthritis (18.4%). Depression was associated with hypertensive diseases (37.4%), respiratory allergy (27.0%), chronic lower respiratory diseases (15.7%), and arthritis (16.4%). Type 2 diabetes was highly associated with hypertensive diseases (75.4%) as well as diseases such as arthritis (26.3%), respiratory allergy (24.7%), and ischaemic heart diseases (23.4%). Cancers were associated with hypertensive diseases (49.7%), respiratory allergy (23.9%), arthritis (22.2%), and ischaemic heart diseases (17.9%). Moreover, diagnoses like arthrosis (15.8%), depression (14.9%), and chronic lower respiratory diseases (14.8%) were common among patients diagnosed with cancer. Being overweight was associated with hypertensive diseases (39.7%), respiratory allergy (26.6%), depression (19.9%), and arthritis (19.3%). Finally, diagnoses such as type 2 diabetes (17.7%) and chronic lower respiratory diseases (17.0%) were common among people with a BMI higher than 35.For further details, the Table 3 presents the overall mean NCCs, the mean NCCs for patients with no education and patients with higher educational attainment, and the prevalence of having 1, 2, 3, 4, 5, 6, or 7+ conditions within each disease across all 199 chronic conditions, disease groups, and overweight. In total, 47 conditions had a mean of 7 or more chronic conditions. Among the 50 chronic conditions with the highest NCCs, 22 conditions were found within disease group I (diseases of the circulatory system) and seven conditions within disease group M . The twenty conditions with the highest mean NCCs were: bronchitis , AMI complex , heart failure , CRF , chronic ischemic heart disease , sequelae of cerebrovascular disease , atherosclerosis , emphysema , osteoporosis in diseases classified elsewhere , complications and ill-defined descriptions of heart disease , AMI , other anaemias , aplastic and other anaemias , other forms of heart disease , aortic , organic, including symptomatic, mental disorders , other diseases of the respiratory system , aortic aneurysm and aortic dissection , atrioventricular and left bundle branch block , and ischemic heart diseases .The largest differences in means between individuals with no educational attainment and individuals with higher educational attainment were found within disease group J (ratio = 1.8)–meaning that individuals with no education had a nearly two times higher mean NCC than individuals with higher educational attainment. The remaining disease group ratios were as follows: Q (ratio = 1.7), B (ratio = 1.7), D (ratio = 1.6), L (ratio = 1.6), K (ratio = 1.5), M (ratio = 1.5), C (ratio = 1.4), H (ratio = 1.4), I (ratio = 1.4), F (ratio = 1.4), E (ratio = 1.3), and N (ratio = 1.2).Among the 50 chronic conditions with the largest differences in means between individuals with no educational attainment and individuals with higher educational attainment, 13 conditions were found within disease group M, seven conditions within disease group H, six conditions within disease group E, four within disease group I and Q, three within disease group C, J and K, two within D and G, and one within F, L, and B. The twenty conditions with the largest differences in means according to educational attainment were: other soft tissue disorders , cystic fibrosis , behavioural syndromes , disorders of ocular muscles , haemorrhoids , ulcerative colitis , congenital malformations of the sexual organs , allergy , disorders of the choroid and retina , coagulation defects , rheumatoid arthritis , soft tissue arthritis , other congenital malformations of the digestive system , disorders of the vitreous body and globe , thyrotoxicosis , disorders of trigeminal nerve and facial nerve disorders , enthesopathies of lower limb , in situ neoplasms , other disorders of the musculoskeletal system , IBS , and hepatitis .chronic conditions, had the highest- and third-highest ratios (2.1 and 2.0) among individuals with no educational attainment and individuals with higher educational attainment.Finally, endometriosis (N80) and overweight (E66), which were not defined as To the best of the authors’ knowledge, the present study is the first and most comprehensive register-based attempt to estimate the multimorbidity disease burden of chronic conditions from a nationwide population using a comparable, uniform methodology across a larger number of chronic conditions. The result section only shows a small fraction of possible examples for some multimorbid data that can be extracted from the catalogues or tables. For instance, and in summary, we investigated the mean NCCs and associations of 14 disease groups, 29 common chronic conditions (to provide the reader with an overview), and 199 chronic conditions for the entire Danish adult population, including differences in sex, age groups, and educational attainments. Our study showed that most people in the Danish population had one or more chronic conditions and that multimorbidity is common. This is in line with previous national and international research , 12, 73.Furthermore, we found a social gradient in the mean of NCCs–with individuals with lower educational attainments having a higher mean. For instance, the largest difference in means of NCCs between individuals with no education and individuals with higher educational attainment was found in disease group J (diseases of the respiratory system). This, increasing NCC by age, higher rates in women and increasing rates of NCC with lower educational attainment are also following earlier studies . We founWhen looking into some examples of associations between the chronic conditions, we found that conditions, not surprisingly, seem to be particularly associated with other diseases within the same disease groups; for example, chronic lower respiratory diseases were highly associated with asthma and respiratory allergy. However, conditions also often transcend disease groups. For instance, hypertensive diseases were also associated with type 2 diabetes and depression, which might be explained by the high prevalence of the three conditions. We found that prevalence rates of depression varied between 14.5% to 51.0% in the 29 conditions–following other evidence showing that depression is a common comorbidity to several chronic conditions , 76. Furseverity and associations of multimorbidity are distributed, including differences in age, sex, and educational attainment across the 199 chronic conditions as described below:The above results, underlined, are not exhaustive but just a few of many possible data extractions. Hence, the main aim of this study was to provide a detailed off-the-shelf catalogue for others to explore their specific interests and needs. In summary, we provided nine comprehensive catalogues Tables  Tables tseverity in terms of the crude mean NCCs. Here, the following tables provide overall mean NCCs, including differences in age groups and sex: We measured disease burden associational prevalence of the chronic conditions. While the means provide a crude estimate of severity, the following tables provide the differentiate treatments [differentiated a prior due to the multimorbidity severity, disease associations, and clusters of common comorbidities, using evidence like the current catalogue. This will make future interventions more data-driven in real-world evidence and multimorbidity directly embedded in medical practice.We see three main potential uses of the catalogue: First, it can support and inform on-the-floor health care specialists of possible multimorbidity concerns to be considered within treatments. Although knowledge about possible multimorbidity is not unknown within medical practice, healthcare systems worldwide are constructed to treat patients with single diseases . This iseatments  and, foreatments –80. We pSecond, we propose that the catalogues are also used to identify and prioritise diseases for treatment based on severity, related disease associations, and clusters of high severity conditions. However, as multimorbidity is only one facet of disease burden, prioritisation should be done in conjunction with other aspects of disease burden, including health-related quality of life , overallThird, we propose that knowledge of multimorbidity severity, chronic condition associations, and clusters of common comorbidities are also used by health care planners to model the future health care systems. We suggest that diseases are seen in a more holistic view, comprising clusters of conditions and that interventions are set up systemically to threaten known and firstly prevalent clusters of conditions; moreover, high severity, multimorbidity, and less prevalent conditions known to be costly with low patient outcomes should be addressed in specialised centres. We propose that the current catalogue is used further to identify relevant clusters of diseases within medical specialities. For instance, the detailed spreadsheet in the One of the main strengths of this study is the data, e.i. the application of data from six nationwide, high-quality registers and the use of the total nationwide population. A second strength is the application of a uniform and comparable methodology as recommended by WHO and researchers , 81, 82,There are, however, also some methodological limitations in the present study. One limitation concerns the methodological issue of defining ‘chronic’. Should ‘chronic’ be understood literally as ‘forever’, and should only ‘severe’ (not in the sense of high mean NCC) diseases be included as suggested by critics , 84? TheMoreover, labelling ICD-10 conditions as chronic or something else to not change the real-world disease burden but merely how we conceptualise it. However, the debate and varying severity highlight the complexity of chronic conditions. Notably, non-communicable diseases or long-term illness may be a better term than ‘chronic condition’, as ‘chronic’ is often understood ‘forever’ in everyday understanding, thus causing confusion or even reluctance.Furthermore, our study showed a lower mean of NCCs for mental conditions like schizophrenia and ADHD and a lower prevalence of cardiovascular diseases like hypertension. There is, however, no clinical reason why mental conditions should have a mean and prevalence below the national averages for hypertension. This indicates that the comorbidities regarding, for example, heart diseases are underreported, and comorbidity could be even higher for conditions within disease group F. Other studies have already discussed similar limitations in underreports of diseases in register data , 65.severity than in terms of crude NCCs [Finally, we recognize that there are other ways to measure disease burden ude NCCs . And thaIt is challenging to provide a broad overview of tendencies and clusters of conditions using solely raw descriptive statistics, particularly for a large number of conditions as in the present study. However, this study provides real-life, detailed estimates without statistical loss of data, particularly for ground health professionals, health care planners and clinicians who need to know their detailed disease population as a first step. Statistical methods, nonetheless, such as latent cluster analysis, factor analysis, multiple pattern analysis and artificial intelligence (AI), might provide a clearer overview as a second step. Although we recognize these statistical methods might have trouble identifying detailed variations and thus identifying subtle tendencies within data, they are still useful to supplement the current catalogue with broader, reduced statistical estimates for overall planning and research purposes. Hence, there is a need for future research to the use and develop consensus on more advanced methods and thereby identify broader clusters of comorbidities, and subtle, possible non-statistical tendencies across conditions and disease groups. Finally, future studies could also investigate how the classic statistical methods perform when identifying clusters and tendencies and comparing these.The current study provides an off-the-shelf catalogue of multimorbidity means, correlational disease prevalence showing the specific disease proportions for 199 different chronic conditions and groups of conditions by gender, age, and educational attainments, based on a complete nationwide population sample. The findings underline that multimorbidity is the rule and not the exception and that multimorbidity is a fundamental condition transcending disease burden and impacting all future treatments. However, current disease guidelines only include multimorbidity at a sporadic level. We argue that having reliable, real-world evidence of multimorbid disease burden is crucial for on-the-floor interventions and health care planners as provided within current study in a raw, descriptive format for others to use. We further suggest that future research identify multimorbidity clusters and investigate how these could best be identified. To the best of the authors’ knowledge, the present study provides the most comprehensive descriptive register study of the means of multimorbidity and correlational prevalence of chronic conditions.S1 Table(DOC)Click here for additional data file.S2 TableNumber of patients, overall mean number of comorbidities and means by sex and age in Denmark on 1 January 2013. Sorted by ICD-10 codes.(DOC)Click here for additional data file.S3 TableNumber of patients, overall mean number of comorbidities and by sex and age in Denmark on 1 January 2013. Sorted by ICD-10 codes.(DOC)Click here for additional data file.S4 TableSorted by ICD-10 diagnosis.(DOCX)Click here for additional data file.S5 TableSorted by ICD-10 diagnosis.(XLSX)Click here for additional data file.S6 TableOverall population estimates and by all educational levels in Denmark on 1 January 2013. Sorted by ICD-10 diagnosis.(DOCX)Click here for additional data file.S7 TableMeans by age and sex groups in Denmark on 1 January 2013. Sorted by ICD-10 codes.(DOC)Click here for additional data file.


Background: The increased rates of postoperative mortality after emergency surgery for obstructive colon cancer (OCC) require the use of risk-stratification scores. The study purpose is to external validate the surgical risk calculator (SRC) and the AFC/OCC score and to create a score for risk stratification. Patients and methods: Overall, 435 patients with emergency surgery for OCC were included in this retrospective study. We used statistical methods suitable for the aimed purpose. Results: Postoperative mortality was 11.72%. SRC performance: strong discrimination (AUC = 0.864) and excellent calibration (11.80% predicted versus 11.72% observed); AFC/OCC score performance: adequate discrimination (AUC = 0.787) and underestimated mortality (6.93% predicted versus 11.72% observed). We identified nine predictors of postoperative mortality: age > 70 years, CHF, ECOG > 2, sepsis, obesity or cachexia, creatinine (aN) or platelets (aN), and proximal tumors (AUC = 0.947). Based on the score, we obtained four risk groups of mortality rate: low risk (0.7%)—0–2 factors, medium risk (12.5%)—3 factors, high risk (40.0%)—4 factors, very high risk (84.4%)—5–6 factors. Conclusions: The two scores were externally validated. The easy identification of predictors and its performance recommend the mortality score of the Clinic County Emergency Hospital of Galați/OCC for clinical use. Colorectal cancer (CRC) is the third most common cancer worldwide. It is the third most common cancer in men and the second in women. There were over 1.9 million new cases of colorectal cancer in 2020, with Hungary having the highest overall rate, followed by Slovakia. Romania was not in the top 10 countries in terms of incidence but was ranked ninth among countries with a high global colorectal cancer mortality rate in 2020 [Acute symptoms, which lead to an emergency surgery, occur in up to 30% of patients with CRC , and 8–1Emergency surgery for OCC is associated with worst postoperative results compared with elective interventions, with an 8–15% mortality rate and even higher (30%) in elderly patients ,7,8.In these patients, with a high postoperative mortality rate, preoperative assessment of surgical risk based on risk scores is required by integrating and interpreting a large amount of clinical information. Many surgical risk scores have been developed to estimate postoperative mortality and, less commonly, specific complications. The most common score used by anesthesiologists—the classification of physical status developed by the American Society of Anesthesiologists (ASA score)—is based on the subjective assessment of the patient’s preoperative health and shows a good correlation with the risk of postoperative mortality .In 2013, the American College of Surgeons developed a surgical risk calculator (SRC) on a sample of NSQIP  in an attempt to accurately stratify patients based on predicting individual risks of mortality and postoperative complications . For theSeveral authors have reported the detection of risk factors or logistic models for the prediction of postoperative mortality after colorectal surgery  score, Association of Coloproctology of Great Britain and Ireland score, and CCR-CARESS score, colorectal POSSUM) ,16,17,18The purpose of the study was to evaluate the performance of the SRC and AFC/OCC scores on our population  by determining the prediction accuracy of the mortality risk as well as the creation of a logistic regression model and an own score that allows the stratification of the risk of postoperative mortality in these patients.We included in the study group the patients with OCC who presented in the emergency room during a period of 10 years, from 2008 to 2017, at the “Sf. Apostol Andrei” Emergency County Clinical Hospital in Galati and who were operated in emergency in the 1st and 2nd Clinic of General Surgery.The observation sheets, the analysis reports, and the operative protocols were retrospectively investigated. The clinical and epidemiological data monitored were age, gender, personal antecedents, comorbidities, functional status expressed using the classification of Eastern Cooperative Oncology Group (ECOG), presence of cachexia or obesity, smoking status, septic condition, and the ASA class assessed by the anesthesiologist. The biological and imaging data analyzed were the values of leukocytes (WBC), platelets, hemoglobin, glycemia, creatinine, proteins, albumins, the presence of electrolyte disorders, acidosis, coagulation disorders, as well as the results of abdominal radiographs and computed tomography.Inclusion criteria: adult patients with OCC, admitted without previous investigations or diagnosis, with emergency open surgery, treated in that period, and with complete recorded data. Exclusion criteria: patients who refused surgical intervention, those with scheduled operations, those who underwent diagnostic laparotomy with or without biopsy, those with incomplete recorded data, and those in whom malignancy was not confirmed.We performed an external validation study of two scores of postoperative mortality: SRC and AFC/OCC score, on a group of patients with emergency surgery for OCC, which analyzed the reliability of these scores. Secondly, we performed logistic regression analysis to identify predictive factors, based on which we created a model and developed a performing score for stratifying the risk of postoperative mortality.https://riskcalculator.facs.org/RiskCalculator/). The dichotomization with the threshold of ≥75 years was performed for the calculation of the AFC/OCC score. Based on the calculated threshold value (>70 years) of the observed mortality, the variable was dichotomized.The age was entered in the online risk calculator according to the age range from the SRC , based on which the variables were dichotomized.Cardiovascular comorbidities included the history of ischemic heart disease, congestive heart failure (CHF), cardiac or vascular surgery, valvular disease, rhythm disorders, hypertension (HTN), or peripheral artery disease. The presence of the following pathology in the patients: HTN, CHF, and hemodynamic failure—defined as all patients who got vasopressors preoperatively or as the appearance of one of the following: systolic arterial pressure < 90 mmHg, mean arterial pressure < 70 mmHg, heart rate (HR) > 120/min, urine output < 15 mL/h, and/or confusion—were registered as qualitative variables.Pulmonary comorbidities were noted in the patients with asthma, chronic obstructive pulmonary disease, emphysema, and a history of pulmonary tuberculosis or lung carcinoma. The presence of dyspnea and a history of severe chronic obstructive pulmonary disease (COPD) were recorded.Renal comorbidities were registered in patients with a history of chronic kidney failure, nephrectomy or polycystic kidney, and kidney transplantation. Acute renal failure was noted for the calculation of the SRC prediction.Neurological comorbidities were recorded in patients with a cerebrovascular event, with or without neurological deficit, history of transient ischemic attack, dementia, neurodegenerative disease, or psychiatric disorder.Patients with diabetes mellitus type I and II disease and insulin-dependent forms were recorded for use in SRC. History of cirrhosis or viral hepatitis was included as hepatic comorbidities.2, respectively >30 kg/m2.Smoker status was recorded and used to calculate SRC prediction. For the assessment of obesity and cachexia, we used the criteria recommended by the World Health Organization based on the body mass index (BMI), obtained by dividing the body mass to the squared of the body height. Cachexia and obesity were recorded in case of a BMI < 18.5 kg/mThe ECOG (Eastern Cooperative Oncology Group) performance status was assessed from 0 to 4. To calculate the SRC prediction, we used grade 0 for functional status—independent, grades 1, 2, 3—partially dependent, and grade 4—totally dependent. We calculated the threshold value for postoperative mortality, and we dichotomized this variable (>2).ASA score was correlated with the questions from SRC: 2—mild systemic disease, 3—severe systemic disease, 4—severe systemic disease/constant threat to life, and 5—moribund/not expected to survive the surgery. For AFC/OCC score calculation, the qualitative variable was dichotomized according to the score factor (≥3), which was equal to the obtained cutoff value (>2) concerning the observed mortality.The laboratory values (white blood cells (WBC), anemia, platelets number, blood sugar, creatinine, electrolyte disturbances, acidosis, and coagulation disorders) were evaluated as qualitative variables, values outside the normal range being considered abnormal values (aN).3; it was necessary to calculate the SRC prediction.Septic status was recorded in patients with OCC with 2 or more of the following criteria: temperature > 38 °C or <36 °C; HR > 90/min; respiratory rate (RR) > 20/min or partial pressure of CO2 (PaCO2) < 32 mmHg; and WBC > 12,000 cells/mmThe intraoperative data included the location of the tumor, the presence of local invasion or metastases, the presence of ischemic lesions of the distended colon, and the presence of peritonitis. The occurrence of intraoperative complications was assessed as qualitative variable.Tumor location was dichotomized into proximal location up to the level of the splenic flexure or distal—distal to the splenic flexure—according to the categorical variable of the AFC/OCC score. We dichotomized the variable according to the anatomical segments of the colon into 8 groups: cecum (C), ascending colon (A), hepatic flexure of the colon (HF), transverse colon (T), splenic flexure of the colon (SF), descending colon (D), sigmoid (S), and rectosigmoid junction (RSJ).The type of surgical intervention was assessed according to the current code of procedural terminology (CPT) used by the SRC: 44,320—diverting stoma, 44,143—Hartman resection, 44,140—partial colectomy with anastomosis, 44,144—partial colectomy with protective ileostomy, 44,150—total colectomy with ileoproctostomy, 45,399—intestinal bypass—unlisted procedure, colon. For the AFC/OCC score, this variable was dichotomized in patients with a stoma  and patients without a stoma—partial or total colectomy with anastomosis or colic bypass. For our analysis, we dichotomized patients with a colostomy (DC), Hartman resection (HP), colic bypass (BP), partial resection with anastomosis (PCA), partial resection with protective ileostomy (PCAI), and total resection with anastomosis (TCA).The duration of the surgical intervention was dichotomized into ≥150 min and <150 min according to the requirement of the AFC/OCC score. Initially, for this variable, we recorded 10 groups of values from 60 min to 300 min. We calculated the threshold value of the duration of the intervention concerning the postoperative mortality and dichotomized the variable according to this (>120 min).The main outcome parameters were: performance evaluation in terms of discrimination and calibration of SRC prediction and AFC/OCC score.Secondary outcome: the creation of our score, evaluation of its performance, and stratification of the mortality risk in the patients included in the study.http://riskcalculator.facs.org/RiskCalculator/, accessed on 1 August 2022) and predicted mortality risks were recorded for each patient.Data were collected for the 20 predictors of SRC: patient demographics , functional and smoking status, weight, height) and comorbidities . Surgical procedures were reviewed and matched to CTP used by the computer. The question “Are there other potentially appropriate treatment options?” was set to “none”, and the “Surgeon risk adjustment” was set to “No adjustment required” for all cases. Data were entered into the SRC online calculator  curves, the observed mortality/estimated mortality ratio (O/E ratio), the Hosmer–Lemeshow goodness-of-fit test (H-L test), the calibration curves, and the Brier score (BS). We generated an ROC curve, and we calculated the area under the curve (AUC) for assessing the prediction accuracy concerning discrimination, whereby the area of 0.5 indicated discriminating power not better than chance, 0.6–0.69 discrimination was considered poor, adequate with an AUC of 0.7–0.79, strong with an AUC of 0.8–0.89, excellent with an AUC of 0.9–1.0, and the area of 1.0 indicated perfect discriminating power. A score is considered reliable within a clinical setting if its AUC value is ≥0.75 .p-value. In addition, for calibration, we graphically represented the predicted score probabilities versus the observed ones. The closer the represented curve is to the straight line—corresponding to the identity function —the better the calibration.We determined the mean predicted postoperative mortality and correlated it to the observed mortality. The observed/expected ratio of 1 represents perfect accuracy of calibration, a ratio < 1 indicates over prediction, and a ratio >1 indicates under prediction. Calibration was further evaluated using the H–L test, defining a lack of fit as a BS was used because of its ability to reflect both discrimination and calibration simultaneously. We determined BS as the average squared difference between the predicted probabilities of events and the observed events . A BS of 0 indicates perfect prediction, whereas a BS of 1 indicates the poorest prediction; a score ≥ 0.25 is considered non-informative . We corrFurther, for the AFC/OCC score, we utilized specific statistics for measuring the performance of a score based on external validation . We calcp-value with the Pearson chi-square, likelihood ratio, and Fisher’s exact test for categorical variables and the Mann–Whitney test for quantitative variables. For continuous variables, we used ROC curves to identify a threshold value. To identify the predictive factors, a univariate logistic regression analysis was performed, specifying the estimated relative risk (OR) and its confidence interval (95.0%CI). Multivariate logistic regression analysis was performed to determine a prediction model for postoperative mortality, specifying for each predictor OR and 95.0%CI, and we evaluated the performance of the model. For the internal validation of the model, we used the bootstrapping method. We calculated the values of the Cox and Snell and Nagelkerke coefficients, the H-L test (p > 0.05 showed good agreement), c-statistic, and BS.The categorical variables were synthesized as frequencies and percentages, and the quantitative variables were as medians and quartiles. We performed statistical correlations, indicating the A new clinical score was built based on the predictors revealed by the model. The performance of this score was analyzed by generating an ROC curve with the calculation of the AUC. We also aimed to assess the optimal threshold value for mortality risk prediction , the sensitivity, and the specificity. The stratification of mortality risk depending on the number of factors present in one patient  was performed using logistic regression analysis.p < 0.05 as a significant difference threshold for all calculations performed. SPSS version 23.0, R version 4.1.1, and Med Calc version 20.111 were used in the statistical analysis.Statistical conclusions were drawn using The study group included 435 patients with an average age of 68.44 ± 11.748, ranging from 27 to 92 years.The causes of death were as follows: septic shock (22) , CHF (16), heart attack (2), anastomotic leakage (6) (in five patients after PCA and one after BP), acute pulmonary failure (3), acute renal failure (1), and intestinal obstruction (1).Postoperative mortality related to the surgical procedure was as follows: 13/140 patients with PCA, with 5 of them associated with anastomotic leakage (9.3%); 6/42 patients with BP, with 1 through the anastomotic fistula (14.3%); 21/169 patients with HP (12.4%); 9/74 patients with DC (12.2%); 2/7 patients with PCAI (28.6%); and 0/3 patients with TCAI.p = 0.000000).The average mortality rate estimated by SRC was 11.80%. The univariate analysis confirmed that mortality predicted by the SRC was significantly associated with the observed mortality , which indicates a strong discrimination potential. With the optimal cutoff value > 12%, SRC identified 88.2% of deceased patients and 71.1% of survivors. Patients with an estimated risk > 12% constitute the group with a high risk for postoperative mortality . The calibration graph is represented in The SRC indicated an excellent calibration of prediction for mortality (11.80% predicted versus 11.72% observed), with the O/E ratio very close to 1 (0.993). When analyzing SRC performance in predicting postoperative mortality depending on the surgical procedure, SRC underestimated mortality in patients with PCA (6.06% versus 9.30%), PCAI (24.49% versus 28.6%), and BP (13.71% versus 14.3%) and overestimated mortality in patients with HP (13.43% versus 12.4%) and DC (17.16% versus 12.2%). The H-L test indicated a good calibration with p = 0.000000).The average mortality value estimated by the AFC/OCC score was 6.93%. The univariate analysis confirmed that mortality predicted by the AFC/OCC score was significantly associated with the observed mortality , which indicates an adequate power of discrimination. With the optimal cutoff value > 4.2%, the score identified 86.3% of the cases that died and only 59.4% of the survivors. Patients with a predicted risk > 4.2% constitute the group with a high risk for postoperative mortality  with O/E > 1. The performance of the score in predicting postoperative mortality depending on the surgical procedure; the score underestimated mortality in all types of procedures: PCA (6.93% versus 9.30%), PCAI (11.3% versus 28.5%), BP (8.25% versus 14.3%), HP (6.28% versus 12.4%), and DC (7.54% versus 12.2%).The mortality rates obtained in the validation group were as follows: in patients without risk factors, 3.17% (2/63) compared to 1.3% (4/313) in the training sample ; with onWe calculated the PSEP on the validation sample (54.82%) and compared it with the value obtained on the training sample (48.7%) .p = 0.0005054) (<0.05). The calibration graph is represented in The H-L test did not indicate a good calibration of the prediction of the score (p = 0.000022), CHF , hemodynamic failure , pulmonary comorbidities , COPD , renal comorbidities , acute renal failure , neurological comorbidities , obesity , cachexia , ECOG > 2 , Davies score > 2 , Charlson score > 2 , age-adjusted Charlson score > 9 , sepsis , WBC  , anemia , platelets  , glycemia  , creatinine  , electrolyte disturbances , acidosis , coagulation disorders , ASA > 2 , operative time > 120 min , and peritonitis   .p = 0.000), CHF , ECOG > 2 , sepsis , obesity , cachexia , abnormal value of platelets , abnormal value of creatinine , and proximal tumors   .p < 0.001), which showed an excellent discriminatory power of the predicted values of postoperative mortality. With the optimal cutoff value > 14%, the model identified 88.2% of the cases that died and 87% of the survivors. Patients with a mortality risk > 14% constitute the high-risk group . This model explained 31.10% of the observed data (Cox and Snell R2) and up to 60.50% (Nagelkerke R2). We determined a c-statistic that was 0.947 and equal to AUC.The model was well calibrated with no significant difference between the predicted and observed probabilities  and less than 0.25, which confirms the accuracy of the prediction.The p < 0.001), which showed an excellent discriminatory power of the values predicted for postoperative mortality. With the optimal cutoff value > 2 factors, the score identified 96.1% of the cases that died and can identify 73.7% among the survivors. Patients with more than two factors for mortality risk constitute the high-risk group  was built based on the nine predictors, in which we assigned 1 point to each of the predictors. Thus, the score for the patients in the group varied between 0 and 6 points (the maximum number of factors present in one patient was 6). To evaluate the performance of the score, we generated an ROC curve. The calculated AUC for this model was 0.932 , and a dedicated score for patients with OCC (AFC/OCC score), developed on 1983 patients. SRC is easy to use and compatible with the type of surgical procedures of the patients in the group , with rather divergent results from the literature on external validation for emergency surgery or emergency colorectal interventions ,30,31,32In the SRC validation of the study, we obtained a strong predictive accuracy in terms of discrimination ability, expressed by an AUC of 0.864, which are similar results to a study on a group of 301 patients with urgent colectomy for various conditions: AUC of 0.8451  or AUC oRegarding the calibration of the SRC prediction, we obtained excellent predictability for postoperative mortality (11.80% predicted versus 11.72% observed) with an O/E ratio of 1.006, unlike a study on patients with emergency operations that present an O/E ratio of 2.48 between observed deaths (11) and those estimated by the SRC (4.42) ; in anotRegarding the type of interventions performed, these reports were different: SRC underestimated mortality after PCA, PCAI, and BP and overestimated mortality after HP and DC, and these discrepancies are explained by the small number of deaths in each subgroup. Obtaining better results than those estimated in patients with CD or HP is explainable because these patients were in a more severe condition, and the choice of the surgical procedure took this aspect into account. The underestimation of mortality in patients with anastomoses can also be explained by the type of intervention that involves assuming complications associated with anastomoses that can sometimes lead to death in patients who presented in a better biological state. The data from the literature regarding the external validation studies of this score present similar situations where the analysis of the subgroups in the batch shows deviations of the prediction rate; the prediction is all the better the more the batch is similar to the NSQIP sample, which contains case mixes .The BS obtained on the batch was 0.080, which is lower than the null model BS 0.103), which indicates the performance of the score. The value of BS obtained was close to the results published by an Italian study (0.072)  but high3, which In the validation study of the AFC/OCC score, we obtained an adequate predictive accuracy of the discrimination power expressed by the AUC of 0.787 > 0.75, being considered reliable within the clinical setting. In comparison with the training sample, where a model with an AUC of 0.80 was obtained, with a sensitivity of 69% and a specificity of 82%, in our batch, we obtained a sensitivity of 86.3% and a specificity of 59.4%—close to 60%—a value taken by some authors as the indispensable lower limit to consider the threshold value (4.2%) informative .p < 0.05, and the aspect calibration graph indicated low calibration, but BS (0.095) confirmed the performance of the model (0.095 < null model BS (0.103) and less than 0.25). Evaluating by BS is considered by some authors [Regarding the calibration of the AFC/OCC score, it underestimated both the mortality rate for the entire batch (6.93% predicted versus 11.72% observed with an O/E ratio of 1.69) as well as the one reported depending on the surgical procedure used. The H-L test indicated del BS 0.3 and les authors . The PSE authors . This me authors .By the obtained results, using the patient group as an external validation sample of the SRC and the AFC/OCC score, we consider that we have fulfilled the first objective of the study. From the point of view of the surgeon, who is found in the situation of choosing which of the two prediction methods to use for estimating the risk of mortality, for a patient with OCC requiring emergency surgery, he would probably choose the AFC/OCC score even though the performance is lower, and it does not offer a personalized risk but only the inclusion in a risk group. It is the dedicated score for patients with OCC who require emergency surgery: the factors necessary to calculate it are identifiable preoperatively, and it is simple to apply. Mortality prediction by SRC is personalized and statistically more efficient, but unlike elective surgery, in which the intervention performed usually coincides with the proposed one, in emergency surgery, quite often the final surgical decision is taken intraoperatively. Therefore, in the estimation of mortality through this score, the type of intervention expected and entered in the computer preoperatively may differ from the one performed. More studies have reported multivariate analysis of predictive factors for postoperative mortality in patients operated on for OCC ,42,43,44Alves et al., in a prospective French multicenter study on a group of 1049 patients with colonic resections for cancer (75%) or diverticulitis, reported a mortality rate of 4.6% and the following predictors of postoperative mortality: age older than 70 years, neurological comorbidities, body weight loss > 10% in <6 months, and emergency surgery .The European Society of Coloproctology reported in a study conducted on a group of over 2000 patients operated on with CRC as predictors for mortality age > 71 years, emergency surgery, open approach, ASA ≥ 3, mechanical anastomosis, intraoperative complications, use of statins, and abnormal creatinine levels .3, potassium < 3.5 mEq/L, sodium < 135 mEq/L, use of steroids, neurological deficits after stroke, disseminated cancer, abnormal values of serum urea nitrogen, alkaline phosphatase, sensory impairment, and low values of serum albumin [Longo et al. identified sixteen predictive factors with a postoperative mortality rate of 5.7% in a group of patients with resections for CRC: ASA ≥ 3, ascites, sodium > 145 mEq/L, platelets < 150,000 mm albumin .Teloken et al., in a study conducted on 249 patients with emergency colon resections, reported after logistic regression analysis that age > 65 years, ASA 4, and cancer were independent predictive factors of mortality at 30 days and a mortality rate of 6.8% .Another study on a group of 776 patients with obstructive right colon cancer reported a postoperative mortality rate of 10% and the following predictive factors of postoperative mortality: age > 70 years, ASA > 3, and hemodynamic instability at admission .In a group of 40 patients with emergency colorectal operations, the authors reported mortality of 12.5%, with the following risk factors: higher ASA score, absence of screening colonoscopy, and postoperative medical complications .On a sample of NSQIP of 11,698 patients with colectomies, of which 29.1% were emergency interventions, postoperative mortality was significantly higher in emergency colectomy 872/3409 (25.6%) versus elective colectomy 567/8289 (6.8%), with risk factors being higher ASA classification and more comorbidities .The mortality rate observed in our study (11.72%) is compared to the data presented closer to the lower limit of the range (6.9–25.6%).In the study group, we identified 26 risk factors for postoperative mortality: age > 70 years, CHF, hemodynamic failure, pulmonary comorbidities, COPD, renal comorbidities, acute renal failure, neurological comorbidities, obesity, cachexia, ECOG > 2, Davies score > 2, Charlson score > 2, age-adjusted Charlson score > 9, sepsis, anemia, abnormal values of WBC, platelets, glycemia, creatinine, electrolyte disturbances, acidosis, coagulation disorders, ASA > 2, operative time > 120 min, and peritonitis. Many of these risk factors are found as predictors of other risk scores for OCC patients with emergency surgery. We did not confirm the type of operation or synchronous metastases as risk factors for postoperative mortality.The five factors of the AFC/OCC score were also confirmed as risk factors for postoperative mortality by univariate logistic regression analysis, with small differences in the OR values: for age ≥ 75 (OR = 3.34 versus 2.35 in the AFC/OCC score training group OCC), hemodynamic failure (OR = 6.79 versus 10.62), pulmonary comorbidities (OR = 5.76 versus 3.45), ASA score ≥ 3 (OR = 6.97 versus 2.14), and proximal tumors  .The nine independent predictors identified within a multivariate logistic analysis model were: age > 70 years, CHF, ECOG > 2, sepsis, obesity or cachexia, abnormal values of platelets or creatinine, and proximal tumors.Advanced age is a well-known risk factor for postoperative death in colorectal emergency surgery ,41,42,45Among all comorbidities, CHF seems to be implicated as a cause of death after surgery in these patients; one out of three patients with CHF died postoperatively (6/18). This predictor (OR = 13.5) proves to be a strong predictive factor in the model, which was also identified in another study on CRC patients .The functional status assessed by the ECOG grade > 2  was a predictor of postoperative mortality also identified by a model that included nonagenarians  and in aSepsis (OR = 14.0) proved to be the strongest predictor of postoperative mortality. There were 18 deaths out of 40 patients with sepsis present at hospital admission, and this was one of the predictors of the SRC score but also of other studies ,46.We identified obesity as a predictor in the model: one out of four obese patients died in the first 30 postoperative days (9/36). Contrary to our results, a recent study did not identify significant differences in postoperative mortality between obese and normal-weight patients with OCC .Another predictor identified was cachexia; approximately one out of three cachectic patients died (30/96). Other studies also identified malnutrition as a mortality risk factor ,47.The abnormal value of platelets, with OR = 11.6, is the fourth predictor of the model, being identified by other authors as a risk factor ,47. The We found, like Manceau in his study, that proximal tumors within the colon were predictors of postoperative mortality; however, other studies that did not identify this association ,51.The model has an excellent discrimination power (AUC = 0.947)—it identifies the high-risk group of patients with an estimated mortality rate > 14%—and a good calibration (H-L test > 0.05). The performance of the own model is better than the performance of other models ,21,52. AIt provides a stratification of the risk of postoperative mortality in four groups depending on the number of factors present in each patient, similarly to other scores ,17,20. TOur score identifies especially those who have a high risk of dying, with the prediction of 100% mortality being reached in patients presenting at least 6/9 factors, unlike the French score, which has a particularly good prediction of survivors, while patients with all five present factors have a prediction of postoperative mortality of only 50% .Therefore, it could be known and applied by the surgeon before the discussion with the patient regarding the informed consent for the surgical intervention. If this score indicates an increased risk of postoperative mortality, the patient’s management should be directed towards a non-surgical procedure, for example, the use of an endoscopic colonic stent or other non-surgical procedures such as a decompression tube insertion in patients with malignant left colonic obstruction. Overcoming the acute phase can allow the patient to undergo a programmed intervention after a possible correction of some risk factors, such as abnormal values of platelets and creatinine, CHF, or sepsis criteria.The limits of the study are related to the retrospective nature, the size of the group, as well as the number of events and the single center of research. Selection bias and the limitations of the observational design study are part of the limitations of the study. To obtain a high-performance model, we used the entire group of patients as a training group, preferring to do the internal validation through the bootstrapping method. Limitations related to the long period of inclusion of cases are driven by possible improvements in health care, which could lead to different outcomes in later years. However, unfortunately, postoperative mortality rates were not significantly different over the whole period. In addition, we believe that this timing issue has a limited effect on our findings because except for sepsis, all predictors of postoperative mortality were unchanged over the study years. During the study period, the definition of sepsis underwent some changes; the classification in the three categories, i.e., SIRS (systemic inflammatory response syndrome), sepsis, and septic shock, required the interpretation of the existing data to assess this predictor.A statistical limitation is the use of the H-L test, which has various flaws, but some authors find it acceptable if the study group > 400 patients. Another limitation would be related to the fact that we assigned equal weights to each risk factor in the own model without taking into account the risk coefficients identified by the model. The decision was made so that it would become as easy as possible to remember and apply the score by the surgeon, which was confirmed after we found that it does not greatly alter the statistical performance .This score could provide reliable assistance in surgical decision making and optimization of peri-operative care. Although the score is internally validated, it is necessary to verify its performance and validate this score on prospective batches or external validation sample.This retrospective study was conducted on an external population of patients with emergency surgery for OCC to validate the SRC, proposed by the ACS, and the score of postoperative mortality after emergency surgery for OCC, proposed by the AFC. SRC demonstrated a strong power of discrimination in the prediction of postoperative mortality and good calibration. Along with the confirmation of the performance of the prediction, another advantage of its application is obtaining a personalized risk, as these are elements that explain its reliability and effectiveness in current practice.For the AFC/OCC score, we obtained an adequate power of discrimination in the prediction of mortality and a reasonable calibration; the easy memorization of the score factors and their simple applications are arguments that support its application to patients with OCC.The stratification of the risk of postoperative mortality with the help of the Mortality Score—Clinic County Emergency Hospital of Galati/OCC could provide reliable assistance in making surgical management decisions , optimizing postoperative care and adequate information of patients and family members. The prediction of 100% mortality in a patient with at least six of the nine factors of the score recommends this score as a good positive predictor of postoperative mortality, which can be especially used for the selection of patients for immediate palliative surgical interventions  or in two-step surgical procedures (HP) or the use of non-surgical procedures in those with very high risk.The easy identification of the score factors, the possibility of preoperative correction of some of them , and the statistical performance obtained in the training group recommend the Mortality Score—Clinic County Emergency Hospital of Galați/OCC for clinical use, but of course, this requires validation on prospective groups or external validation groups.


Atopic diseases and atrial fibrillation (AF) seem to share an underlying inflammatory pathology. To date, some population-based studies have explored the relationship between the two. We aimed to conduct a meta-analysis to examine the role of atopic condition in AF risk.2 and Cochran Q test were used to evaluate the heterogeneity.All relevant observational studies in PubMed and EMBASE databases up to November 2021 were searched. In RevMan 5.3, we used random-effects or fixed-effects models to pool the effect sizes of hazard ratio (HR), odds ratio (OR) and their corresponding 95% confidence intervals (95% CI). In addition, In = 4; 95%CI, 1.25–1.58), 1.12  and 1.06 , respectively.A total of 2488 records were retrieved. After screening according to the predetermined criteria, 6 cohort studies and 2 case-control studies were included in this meta-analysis. Herein, the meta-analysis of 6 cohort studies suggested that atopic diseases potentially increased the AF risk with the pooled HR of 1.26 , while the pooled effect size  of 2 case-control studies was not statistically significant. Based on the types of atopic diseases, further subgroup analyses of 6 cohort studies revealed that asthma, allergic rhinitis, and atopic dermatitis all potentially increased the risk of subsequent AF with the pooled HR of 1.41 (This meta-analysis demonstrated that patients with atopic diseases have a higher risk of developing AF, particularly those with asthma. Atrial fibrillation (AF) is the most common type of cardiac arrhythmia in adults, with an increasing trend in both morbidity and mortality . The recAtopic diseases are primarily composed of asthma, allergic rhinitis, and atopic dermatitis, and may coexist in the same individuals. Specifically, allergic rhinitis mainly involves the upper airway and is characterized by nasal congestion, rhinorrhea, sneezing, and nasal itching . Asthma Emerging evidence suggested that inflammation and immune response are involved in the initiation and maintenance of AF . MultiplThe statement of Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) was followed to perform the meta-analysis . BesidesTwo investigators  independently retrieved relevant articles in PubMed and EMBASE from inception to the search end . The search terms were mainly involved with “atrial fibrillation” and “atopic diseases.” We did not restrict language, region of study, or sample size for publications retrieved. The details of our search strategy are summarized in Inclusion criteria: (1) observational (cohort or case-control) studies; (2) cohort studies must include patients with atopic diseases and non-atopic individuals. And, the relative risk (RR) or hazard risk ratio (HR) with 95% confidence intervals (95% CI) of incident AF comparing between patients with atopic diseases and individuals without atopic diseases must be reported. (3) case-control studies must consist of patients with AF and controls. Besides, the studies also need to trace their allergic history and provide odds ratio (OR) with 95%CI of the association between atopic diseases and AF. Exclusion criteria: (1) basic experimental researches; (2) lacking or not available data; (3) case reports, reviews, letters, replies, or comments; (4) duplicate publications of the same database.For all qualified studies, a standardized collection list was used to extract the following details: first author’s name, publication year, country of the study, study design, the age of participants, sample size, the approaches of disease definition, variables adjusted in multivariate analysis.Next, the Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of all included studies. For any controversial contents, the three researchers  discussed and made decisions together. The details of scoring were outlined in 2, respectively. In detail, the value of I2 was divided into 4 grades, namely high heterogeneity (>75%), moderate heterogeneity (51–75%), low heterogeneity (26–50%), and insignificant heterogeneity (<25%) (I2 > 50%), a random effect model was used to pool the effect size. What’s more, we further used sensitivity analysis to find the sources of high heterogeneity.The effect sizes of HR, OR, and their corresponding 95%CI were extracted as the primary analysis statistics. In addition, based on study design and the types of atopic diseases, we performed subgroup analyses to clarify the association between atopic diseases and the risk of AF. For the heterogeneity of included studies, we assessed it qualitatively and quantitatively using Cochrane Q test and Iy (<25%) . During 1 and STATA 15.0 . Two-sided p < 0.05 was viewed as statistically significant.In this study, data were analyzed using the software of RevMan 5.3A total of 2488 records were retrieved from the databases of PubMed and EMBASE. After eliminating duplicates, the 2353 records were reviewed with titles and abstracts. After initial screening, 2312 articles were excluded because they were inconsistent with the scheduled criteria. Then, we conducted a comprehensive review of the full text of 41 articles and eliminated 33 articles because of insufficient results, or repeated publication of the same database. Eventually, a total of 8 studies (6 cohort studies and 2 case-control studies) , 18–23 wThe included six cohort studies consisted of 1,452,112 patients with atopic diseases and 7,373,745 comparators. Three studies were conducted in Europe, 2 in Asia, and 1 in North America. Regarding specific types of atopic diseases, 4 studies investigated the association between asthma and AF, 3 studies examined the relationship between atopic dermatitis and AF, and another study involved allergic rhinitis and AF. All 6 studies extracted the effect size of HR, with 5 providing adjusted HR and 1 providing crude HR.Among the 2 case-control studies, one from Asia (China), and the other from North America (United States). In total, they traced the history of asthma in 8,869 patients with AF and 11,505 controls, and used the adjusted OR (aOR) to assess the relationship between the two.Both cohort and case-control studies mainly focused on adult patients, and the definitions of AF and atopic diseases  were based on matching International Code Designator, documentation of medication, self-reported or physician recorded diagnosis of disease. Moreover, based on the four characteristics: daytime symptoms (2 times per week or less or more than 2 times per week), night awakenings (none or any), the need for reliever medication (2 times per week or less or more than 2 times per week), and limitation of activities (none or any), Cepelis et al.  classifiI2 = 95%, p < 0.001) ; nevertheless, the high heterogeneity was observed (< 0.001) .2 of 78% , and the heterogeneity was also high with I2 of 78% .I2 = 75%) (I2 = 0%)  . For atoI2 = 0%) . Only onI2 = 0%) .I2 = 0%) and 1.34 , respectively .Given the limited number of included studies, we assessed publication bias in the subgroups of cohort studies and asthma cohort studies. There was no obvious publication bias in either subgroup , especially those in persistent or uncontrolled condition. Allergic rhinitis and atopic dermatitis were second to asthma in their association with the risk of new-onset AF .Currently, the potential mechanism that could explain the association between atopic diseases and AF may be chronic systemic inflammation. As we all know, after exposure to environmental allergens, epithelial-derived alarmins  could activate T helper type 2 (Th2) cells and group 2 innate lymphoid cells (ILC2s). Then these immune cells would release a series of cytokines, such as IL-4, IL-5, and IL-13, which promote the production of immunoglobulin E (IgE), eosinophilic inflammation . In addiSecondly, mast cells, a key regulator of allergy and inflammation, are also abundant in the human heart . When alIn this meta-analysis, asthmatics were identified as having a higher risk of developing AF. Due to the different definitions of asthma subtypes, we did not perform relevant subgroup analyses. But it is worth noting that Cepelis et al.  found a The relationship between atopic dermatitis and cardiovascular disease is controversial . Our metThere are some limitations should be considered when the results of our meta-analysis were interpreted. First, the small number of studies available for inclusion limited the possibility of conducting more subgroup analyses to some extent. Second, the definition of allergic diseases and AF lacks preciseness and specificity. For instance, all the included studies did not distinguish between paroxysmal and persistent AF, so we cannot make a further clinical interpretation. Third, the medication of patients with atopic diseases is a factor that must be considered for their secondary diseases. However, only 2 studies (one cohort study and one case-control study) performed subgroup analyses of the association between medications (β-agonist and corticosteroid) for asthma control and AF risk and demonstrated the positive relationship between the two. In the future, it is necessary to obtain relevant clinical information to clarify the causes of AF in patients with atopic diseases. Besides, the study of Yang et al. 2011  that hadIn summary, three atopic diseases  were noted to increase the risk of AF. In the future, more high-quality cohort and case-control studies should be conducted to confirm the association between atopic diseases and AF, with particular attention to whether the increased risk of different types of AF is related to the type, severity, and treatment of atopic diseases.The original contributions presented in this study are included in the article/RZ, JW, JZ, and LD proposed and designed the study. RZ, JW, and ZL contributed to the data collection, statistical analysis, and quality assessment. RZ and JW interpreted the data and wrote the original manuscript of the study. ZL, JZ, ZW, and CX conducted major revisions. All authors critically revised important intellectual content of the study and approved the final version for publication.