Disease trajectory and mortality among sepsis patients: a prospective cohort study

Disease trajectory and mortality among sepsis patients: a prospective cohort study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Disease trajectory and mortality among sepsis patients: a prospective cohort study Chunyang Li, Chao Zhang, Bo Wang, Jie Chen, Wenyi Zhang, Zhiye Ying, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5886414/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 14 Oct, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted 7 You are reading this latest preprint version Abstract Sepsis is a life-threatening disease and among the most common cause of death, which influence a series of following medical conditions. A comprehensive analysis of the cross-talks with temporary order of disease trajectory or trajectory led to cause of death following sepsis remains unclear. By using data from population-based UK Biobank, 8647 septic patients matching with 86386 controls without sepsis were included. Individuals were followed-up from 1 month after the index date until the end of 2019 with linkage of inpatient or death records to the registers. Then conditional Cox regression, binomial test together with conditional logistic regression were conducted to visualize the disease trajectories and trajectories leading to cause of death in sepsis survivors. During a median follow-up of 3.99 years, sepsis significantly increased the risk of 113 subsequent medical conditions. By visualizing disease-disease associations with time-dependent sequence, we identified four main affected disease clusters after sepsis, including circulatory, metabolic, respiratory and genitourinary system disease, further linking a series of downstream health outcomes. We also identified trajectories leading to mortality in three major categories of death in sepsis survivors, which were neoplastic, circulatory and respiratory system disease. In addition, disease trajectory after sepsis differed in gender and age groups were also explored in our study. These trajectory networks visualize a series of pathways linking sepsis to a broad range health conditions and provide potential intervention targeting these diseases for inhibiting adverse events in sepsis patients. Sepsis disease trajectory mortality cause of death disease network Figures Figure 1 Figure 2 Figure 3 Figure 4 1. Introduction Sepsis is a complex and life-threatening medical condition, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection [ 1 ]. According to estimates by the World Health Organization (WHO), there were approximately 48.9 million cases of sepsis and 11 million (almost 20%) deaths related to sepsis worldwide in 2020 [ 2 ], sepsis is the most common cause of in-hospital deaths [ 3 ]. Patients who survive sepsis often have long-term physical and psychological declines as well as cognitive disabilities, which can significantly impact their quality of life [ 4 ]. Sepsis has been widely reported to increase the risks of subsequent acute kidney injury (AKI) [ 5 ], myocardial infarction, stroke [ 6 ], transient ischemic attack [ 7 ], pneumonia and chronic obstructive pulmonary disease (COPD) [ 8 ]. In addition to somatic disease, sepsis also leads to persistent cognitive impairment and functional disability [ 9 ], for example dementia [ 10 ], anxiety, depression and posttraumatic stress disorder [ 11 ]. Diabetes and hypertension may affect following cardiovascular disease in sepsis survivors through either direct or indirect association with other comorbidities or change inflammation cascade [ 12 ]. However, previous studies mainly focused on separate diseases, and no comprehensive investigation about the cross-talks of the disease conditions derived from sepsis has been conducted. Importantly, the subsequent diseases caused by sepsis may have a temporal order and form a complex disease network influencing each other. Therefore, to prevent future health declines and improve quality of life in septic patients, it is important to comprehensively understand the disease trajectories and networks of sepsis. Recently, disease trajectory analysis has been widely used to investigate the cross-talks among depression [ 13 ], anxiety and stress-related disorders [ 14 ], nonalcoholic fatty liver disease [ 15 ] and breast cancer [ 16 ]. By visualizing possible emerged diseases with their temporal order and depicting the disease-disease association with magnitude, one can examine the sequential patterns and causal relationships after a certain disease origin. By using population-based UK Biobank, we mainly aimed to investigate the subsequent medical conditions and disease trajectory network with temporal order after prior diagnosis of sepsis. We further explored and compared trajectories following sepsis diagnosis separately in different gender and age groups. 2. Methods 2.1 Study design In brief, UK Biobank is an ongoing population-based cohort recruited approximately 500,000 participants from England, Scotland and Wales, aged 40–69 at the time of recruitment between 2006 and 2010. Baseline data about participants’ demographic characteristics, socioeconomic status, behavioral, lifestyle factors, medical history and medications were collected in 22 research assessment centers across the UK at the recruitment. Follow-up is conducted chiefly through linkages to Hospital Episode Statistics database for England, the Scottish Morbidity Record (SMR) for Scotland and Patient Episode Database for Wales. Death registry records were accessed by linking NHS for England and Wales, as well as NHS Central Register, National Records of Scotland [ 17 ]. In the present study, based on the UK Biobank data, among the 502,507 participants, we first excluded 194 individuals who withdrew their informed consent forms, 623 participants with missing values for Townsend deprivation index, sex and birth year. Then, among the remaining 501,690 participants, individuals that were diagnosed with sepsis before December 31, 2019 were included in the exposed cohort. The diagnosis was ascertained by inpatient records. To preclude the potential reverse association between sepsis and other acute disorders, patients with any other diseases diagnosed within 1 month prior to the sepsis diagnosis were excluded. For chronic conditions, we hypothesized that patients underwent comprehensive examinations during their hospital stay, thus, diseases not presented within 1 month before sepsis diagnosis shall be ruled out as pre-existing disorders and could be treated as candidates for further trajectory analysis. We then randomly selected up to 10 sepsis-free controls from UK Biobank, and individually matched to the sepsis patient by birth year, sex, and Townsend deprivation index (transformed to tertiles) [ 18 ]. Finally, 95,033 individuals were included in the study cohort, including 8,647 septic patients and 86,386 matched sepsis-free controls (Fig. 1 ). The characteristics of sepsis and unexposed control groups at the index date were summarized and compared. Continuous variables were presented as median with interquartile range (IQR) and compared using analysis of variance (ANOVA) tests, and categorical variables were reported as number with percentage and compared by Chi-square tests. Follow-up for all participants started from 1 month after the index date, and until death, loss to follow-up or the end of study (December 31, 2019), whichever occurred first. For the exposed sepsis patients, the index date was designated as the date for sepsis diagnosis, and the index date of unexposed controls were the same as their matched sepsis patients. The flow chart for inclusion and exclusion of participants was presented in Fig. 1 . 2.2 Ascertainment of sepsis Sepsis patients were defined as individuals with a first-time hospitalization for sepsis (primary or secondary diagnosis), using explicit diagnosis ICD-10 codes (provided in Additional file 1: Table S1 ) [ 2 ]. In order to eliminate the potential influence of the Coronavirus Disease 2019 (COVID-19) on disease occurrence, sepsis inpatients were included from January 1, 1997 and December 31, 2019 [ 19 ]. For sepsis patients, the index date was defined as the date of the first sepsis diagnosis. For the unexposed controls who were matched to sepsis patients, the index date was the same as the date of sepsis diagnosis for their matched cases. 2.3 Diagnoses of other medical conditions Diagnoses of other medical conditions were retrieved from the main and secondary diagnoses in the UK Biobank inpatient hospital, ICD-9 codes were transformed to ICD-10 codes [ 20 ]. Diagnoses related to pregnancy, childbirth, perinatal conditions, and unclassified symptoms or signs were excluded, and analyses were restricted to Chaps. 1–14 of ICD-10 codes. We used the 3-digit ICD-10 codes and combined conditions with clinical or biological similarities for medical condition identification (Additional file 2: Table of ICD-10 codes) [ 13 ]. Thus, a total of 469 medical conditions were examined in the trajectory analysis. If multiple records were identified for the same individual, only the first record and corresponding date were maintained for analysis. 2.4 Death The cause of death was recorded in the death registry in UK Biobank with ICD-10 codes [ 21 ]. based on Chaps. 1–14 of ICD-10 codes, the causes of death were classified into 13 categories, namely death due to infectious diseases, neoplasms, hematopoietic diseases, endocrine/metabolic disease, mental disorders, diseases of the nervous system, diseases of sense organs, circulatory system diseases, respiratory system diseases, digestive system diseases, dermatologic diseases, musculoskeletal system diseases, and genitourinary system diseases (codes were listed in Additional file 2: Table of ICD-10 codes). 2.5 Statistical analysis 2.5.1 Trajectory analyses of sepsis Disease trajectory following sepsis was constructed using three interrelated steps which have been previously described [ 13 , 15 , 16 , 22 ]. Firstly, Phenome-wide association analysis (PheWAS) using conditional Cox regression was performed to investigate the risks of 469 medical conditions among participants with sepsis compared to their matched unexposed individuals. Follow-up of the individuals in the sub-cohort started from 1 month after sepsis diagnosis, and ended on date of the medical condition, date of death or December 31, 2019, whichever came first. To identification of increased risks of medical conditions after sepsis, diseases occurred in at least 100 sepsis patients, with a p value less than the Bonferroni correction threshold (0.05/469 = 0.000106) and hazard ratio (HR) greater than 1, were considered in the second step of the analysis. In the second step, we conducted binomial tests to explore whether the pattern of a later diagnosis (D2) occurring after another previous diagnosis (D1) was dominant, or the proportion of patients bearing this pattern exceeded 50%, among those with both D1 and D2 diagnoses. These tests were performed on all possible D1 and D2 pairs with a temporal order that occurred in at least 100 sepsis patients. A binomial test p value < Bonferroni correction threshold was included in the next step. Thirdly, nested case-control study using conditional logistic regression was performed to evaluate the association between D1 and D2. Disease pairs passing Bonferroni corrected threshold with odds ratio (OR) > 1, and occurred in at least 50 sepsis patients had D1 and D2 diagnosis after sepsis diagnosis were considered as significant (details were listed in Additional file 1: Methods). Similar methods using the three interrelated steps as described above were conducted to investigate the trajectory leading to death in sepsis survivors, with restricting D1→D2→Death pairs occurred in at least 25 sepsis patients for binomial test and 10 sepsis patients for nested case-control study (see in Additional file 1: Methods). The trajectory network was formed by combining disease pairs with overlapping diseases. For example, disease pairs D1→D2 and D2→D3, with overlapping D2, were combined with the trajectory D1→D2→D3. The flowchart of disease trajectories selection following sepsis diagnosis and trajectories selection were shown in Additional file 1: Figs. 1 and 2 , respectively. 2.5.2 Subgroup analysis To investigate whether the disease trajectory of sepsis was differed by gender and age at index date, the disease trajectory after sepsis was analyzed separately in these subgroups. Herein, we divided sepsis patients into two different age groups according to the median age (which is 66.96), then sepsis patients aged ≤ 66.96 together with their matched controls were categorized into younger group while sepsis patients aged > 66.96 and their matched controls were grouped as older group. By using the same analyses for disease trajectory and trajectory leading to death, we adjusted the threshold for disease trajectory analysis for reduced sample size in subgroup analysis (temporary order of D1→D2 pairs occurred in at least 50 sepsis patients, or D1→D2→Death pairs occurred in at least 10 sepsis patients). The flowchart of sex- ang age- specific disease trajectories and trajectories leading to death after sepsis were shown in Additional file 1: Figs. 3 and 4 , respectively. 2.5.3 Sensitivity analysis In order to test the robustness of diseases associated with sepsis identified in our study, we repeated PheWAS analyses by 1) excluding individuals with a history of any disease belonged to the same category of the disease outcomes. For example, to explore the association between sepsis and following hypertension, individuals with any cardiovascular disease before index date were excluded; 2) additionally adjusting Charlson comorbidity index (CCI) on the index date to control the impact of comorbidities at baseline (definition of CCI was listed in Additional file 1: Table S2 ) [ 23 , 24 ]; 3) maintaining sepsis patients by defining sepsis with primary diagnosis in UK Biobank; 4) excluding sepsis patients with critical care records at the index episode (these patients were identified as severe sepsis patients), to eliminate the influence of sepsis heterogeneity on the subsequent risks of medical conditions. 5) excluding sepsis patients had any other diseases diagnosed within 6 months or 1 year prior to the sepsis diagnosis. 6) the subdistribution HR of 469 following medical conditions were estimated using Fine-Gray competing risk model, with death treated as the competing risk. 7) sepsis patients were individually matched to hospitalized sepsis-free controls from UK Biobank based on the same birth year, year of sepsis diagnosis (for inpatient controls this was defined as the year at the first inpatient admission), sex, gender, and Townsend deprivation index (in tertiles). To further validate the confidence of disease trajectories in our study, following sensitivity analyses were performed: 1) identification of trajectory using sequential pattern mining with cSPADE algorithm [ 25 ], which could find sequential patterns (for example, the disease trajectory of “sepsis→hypertension”) with higher frequency of incidence as compared to all the patterns derived from dataset. We included sequential disease patterns occurred in at least 1% sepsis patients to detect patterns with rare frequency [ 26 ]; 2) searching Danish Disease Trajectory Browser ( http://dtb.cpr.ku.dk/ ) using corresponding ICD-10 codes identified in our study, the threshold set herein were length ≥ 2, relative risk ≥ 1 and number of patients ≥ 20. This online platform could visualize disease trajectories in browser using Danish National Patient Registry, comprising 7.2 million patients and 122 million admissions with ICD-10 codes recorded [ 27 ]. 3. Results 3.1 Baseline characteristics Of the 8,647 sepsis patients and their 86,386 matched individuals, median age was 66.92 on the index date, and most of them were male (50.63%). As shown in Table 1 , the age, sex, and Townsend deprivation score was appropriately balanced for the exposed cohort and the matched cohort. Table 1 Characteristics of the sepsis patients and their matched individuals without sepsis. Total (n = 95,033) Sepsis patients (n = 8,647) Matched individuals without sepsis (n = 86,386) P value Age at index (median with IQR), years 66.92 (58.25–72.85) 66.96 (58.25–72.87) 66.91 (58.24–72.85) 0.92 Sex 0.99 Female 46917 (49.37%) 4268 (49.36%) 42649 (49.37%) Male 48116 (50.63%) 4379 (50.64%) 43737 (50.63%) Townsend deprivation index 0.99 High 38980 (41.02%) 3548 (41.03%) 35432 (41.02%) Medium 30009 (31.58%) 2730 (31.57%) 27279 (31.58%) Low 26044 (27.41%) 2369 (27.40%) 23675 (27.41%) Follow-up time (median with IQR), years 3.99 (1.79–8.81) 3.12 (1.21–8.05) 4.08 (1.84–8.88) < 0.001 Charlson comorbidity index 3 1989 (2.09%) 893 (10.33%) 1096 (1.27%) ANOVA test was performed for continuous variables. χ 2 test was performed for category variables. 3.2 Medical conditions and causes of death associated with sepsis The median follow-up time for the whole cohort was 3.99 years [interquartile range (IQR), 1.79–8.81]. As expected, among 469 medical conditions, sepsis was associated with more frequent occurrences of 113 diseases (Fig. 2 and Additional file 1: Table S3), including genitourinary system diseases (i.e., chronic kidney disease (CKD) (HR 3.83, 95%CI 3.45–4.26, P < 0.001)), metabolic system disease (i.e., diabetes (HR 1.63, 95%CI 1.45–1.84, P < 0.001)), circulatory system disease (i.e., hypertensive disorder (HR 1.60, 95%CI 1.48–1.73, P < 0.001)) and respiratory system disease (i.e., COPD (HR 3.38, 95%CI 3.02–3.79 P < 0.001)), et al . We also observed an association between sepsis and subsequent diagnosis of several infectious diseases. For instance, sepsis was associated with bacterial intestinal infections (HR 6.43, 95%CI 5.11–8.09, P < 0.001), and infectious gastroenteritis and colitis (HR 5.19, 95%CI 4.69–5.74, P < 0.001), with a median interval of follow-up of 3.9 years after the diagnosis of sepsis. In addition to somatic diseases, sepsis also had significant impact on subsequent mental disorders, such as alcohol abuse (HR 2.21, 95% CI 1.80–2.71, P < 0.001), tobacco abuse (HR 1.93, 95% CI 1.70–2.19, P < 0.001), depression (HR 2.49, 95% CI 2.24–2.78, P < 0.001), and anxiety (HR 2.27, 95% CI 2.01–2.56, P < 0.001). For the cause-specific mortality, except disease of sense organ (N death =0), sepsis was significantly associated with increased risk of deaths due to majority categories of diseases, with the exception of musculoskeletal system diseases and hematopoietic diseases (Table 2 ). Table 2 The association between sepsis and causes of death. Causes of death Total (N = 95033) Female (N = 46917) Male (N = 48116) Younger individuals (N = 47562) Older individuals (N = 47471) Number a HR (95% CI) Number b HR (95% CI) Number c HR (95% CI) Number d HR (95% CI) Number e HR (95% CI) Infectious disease 181 31.47 (23.32–42.47) * 74 52.11 (29.43–92.27) * 107 24.62 (17.23–35.18) * 75 30.56 (19.30–48.40) * 106 32.15 (21.65–47.78) * Neoplasms 1122 8.86 (8.17–9.61) * 472 9.84 (8.67–11.18) * 650 8.26 (7.43–9.17) * 469 7.44 (6.60–8.40) * 653 10.26 (9.19–11.46) * Endocrine/metabolic disease 259 11.53 (9.63–13.81) * 95 17.89 (12.75–25.09) * 164 9.55 (7.69–11.85) * 122 12.75 (9.74–16.71) * 137 10.62 (8.33–13.53) * Mental disorders 177 10.32 (8.35–12.76) * 59 9.33 (6.53–13.33) * 118 10.90 (8.37–14.21) * 34 5.09 (3.36–7.72) 143 13.64 (10.57–17.60) * Diseases of the nervous system 190 9.94 (8.12–12.18) * 59 8.01 (5.68–11.30) * 131 11.17 (8.68–14.38) * 51 6.49 (4.55–9.26) * 139 12.37 (9.62–15.91) * Circulatory system disease 704 7.99 (7.23–8.83) * 233 10.91 (9.05–13.15) * 471 7.05 (6.26–7.94) * 260 6.61 (5.64–7.74) * 444 9.10 (8.00-10.36) * Respiratory system disease 670 10.99 (9.84–12.28) * 228 13.53 (11.07–16.53) * 442 10.01 (8.76–11.44) * 233 9.89 (8.24–11.88) * 437 11.68 (10.16–13.43) * Digestive system disease 243 15.66 (12.78–19.17) * 88 19.20 (13.41–27.51) * 155 14.17 (11.07–18.12) * 123 17.74 (13.19–23.86) * 120 13.97 (10.57–18.46) * Musculoskeletal system disease 78 16.36 (11.36–23.56) 36 23.30 (12.75–42.57) 42 13.01 (8.18–20.71) 24 15.57 (8.16–26.69) 54 16.74 (10.76–26.05) * Genitourinary system disease 234 17.55 (14.15–21.76) * 81 24.72 (16.42–37.23) * 153 15.19 (11.78–19.60) * 94 24.67 (16.86–36.10) * 140 14.69 (11.29–19.12) * Hematopoietic disease 47 18.95 (11.59–30.99) 16 26.17 (10.24–66.89) 31 16.56 (9.26–29.61) 22 26.66 (11.86–59.88) 25 15.10 (8.06–28.30) Disease of sense organ 0 NA 0 NA 0 NA 0 NA 1 NA Dermatologic disease 31 30.35 (14.88–61.94) 15 29.34 (10.65–80.79) 16 31.37 (11.49–85.65) 12 58.69 (13.13-262.29) 19 23.26 (10.18–53.17) * Association with HR > 1, p value < the Bonferroni correction (0.05/13 = 0.00384615) and occurred in at least 100 sepsis patients (50 for female or male patients, and 50 for younger or older patients), was considered as significant association between sepsis and causes of death as compared to individuals without sepsis. a Number of septic patients who died due to the causes of death, including 8647 sepsis patients and 86386 unexposed controls. b Number of female septic patients who died due to the causes of death, including 4268 sepsis patients and 42649 unexposed controls. c Number of male septic patients who died due to the causes of death, including 4379 sepsis patients and 43737 unexposed controls. d Number of younger septic patients (aged ≤ 66.96) who died due to the causes of death, including 4325 sepsis patients and 43237 unexposed controls. e Number of older septic patients (aged > 66.96) who died due to the causes of death, including 4322 sepsis patients and 43149 unexposed controls. 3.3 Temporary disease trajectories in sepsis patients Among 113 medical conditions associated with sepsis, 130 pairs were identified (Additional file 1: Table S4). Among the disease pairs with temporary order, four major patterns were identified (Fig. 3 ). In Cluster 1 (Fig. 3 A), the intermediate diseases after sepsis mediating most of downstream diseases were hypertensive disorders, atrial fibrillation and flutter, and ischemic heart disease, then these circulatory system diseases further led to other diseases like heart failure, pneumonia and acute renal failure with subsequent CKD. Similarly, in Cluster 2 (Fig. 3 B), the intermediate disease was diabetes, further resulted in hypertensive disorders with subsequent obesity, cardiac arrhythmias and cerebrovascular diseases. In Cluster 3 (Fig. 3 C), conditions such as urinary system infection and acute renal failure were the most prevalent following sepsis, and a trajectory from acute renal failure to CKD was observed after sepsis. Cluster 4 (Fig. 3 D) mainly included respiratory system disease (i.e. asthma or COPD) after sepsis, further associated with following musculoskeletal system disease including osteoarthritis and joint disorders. The overview of other disease trajectories after sepsis was presented in Additional file 1: Fig. 5. 3.4 Temporary disease trajectories leading to death following sepsis Totally, based on 13 D1→D2→Death pairs were identified (Additional file 1: Table S5), we identified three major networks linking sepsis to death due to neoplasms, circulatory system diseases and respiratory system diseases (Fig. 4 ). Hypertensive diseases occurred after sepsis, which then led to heart failure, atrial fibrillation and flutter as well as acute renal failure, eventually causing death due to circulatory system diseases (Fig. 4 A). Sepsis caused acute renal failure and metastatic cancer, which eventually resulting in death due to neoplasms (Fig. 4 B). Additionally, sepsis triggered hypertensive diseases and COPD, which linked to subsequent pneumonia and respiratory failure, culminating in death due to respiratory system disease (Fig. 4 C).. 3.5 Subgroup analyses Characteristics of female and male individuals were listed in Additional file 1: Table S6. Medical conditions associated with prior sepsis using PheWAS analysis in female and male were largely similar, but sepsis increased subsequent risks of some diseases including chronic rheumatic heart disease and stroke were only observed in male (Additional file 1: Table S7). More disease trajectories after sepsis in male were observed that there were totally 82 and 106 disease pairs in female and male, respectively (Additional file 1: Table S8). Both female and male sepsis patients were associated with an increased risk of subsequent diabetes, hypertensive disorders, COPD and asthma (Additional file 1: Fig. 6). However, the trajectory from sepsis to ischemic heart disease in females was primarily mediated by hypertensive disorders, whereas the progression in males was mainly mediated by diabetes. Additionally, ischemic heart disease in males was associated with a significantly greater number of downstream medical conditions compared to that in females. Conversely, certain trajectories following sepsis, such as the progression from acute renal failure to CKD was only observed in female survivors. In female sepsis patients, only one trajectory leading to death was observed, which progressed from hypertensive disorders to disorders of fluid, electrolyte, and acid-base balance, ultimately culminating in circulatory death. For male patients, two major causes of death were identified, such as circulatory death attributed to a trajectory from hypertensive disorders to respiratory failure, and respiratory death due to a trajectory from heart failure to respiratory failure (Table 2 , Additional file 1: Table S9 and Fig. 7). Characteristics of young and old participants were listed in Additional file 1: Table S6. More disease trajectories were observed in younger sepsis patients (aged ≤ 66.96 years) compared to older patients. Disease trajectories originating from hypertension after sepsis, followed by depression and anxiety diagnosis, were only observed in younger patients (Additional file 1: Fig. 8 and Table S10 and Table S11). The risk of death for younger sepsis patients increased across 8 different categories of causes, whereas for older sepsis patients, the mortality risk increased across 10 categories (Table 2 ). Interestingly, a greater number of trajectories leading to death were observed among younger sepsis patients. For instance, in younger sepsis patients, sepsis followed by COPD led to respiratory failure, ultimately resulting in respiratory death. In addition, sepsis followed by hypertensive disorders also led to acute renal failure and subsequent circulatory death (see Additional file 1: Fig. 9 and Table S12). In contrast, for older sepsis patients, the trajectory to respiratory system death was from pneumonia to respiratory failure (see Additional file 1: Fig. 9 and Table S12). 3.6 Sensitivity analyses As shown in Additional file 1: Table S3 (sensitivity analysis 1 and 2), most of the PheWAS results remained largely unchanged when excluding individuals with a history of any disease belonged to the same category of the disease outcomes or additionally adjusting for CCI at baseline. When maintaining sepsis patients defined by primary diagnosis codes in UK Biobank (totally 40682 participants, of which 3702 sepsis patients and 36980 matched individuals without sepsis), 102 associations between sepsis and subsequent medical conditions remained significant (Additional file 1: Table S3, sensitivity analysis 3). After excluding sepsis patients who had critical care records (severe cases), 87679 individuals (7978 sepsis patients and 79701 their matched controls) left, and 107 significant associations between sepsis and subsequent medical conditions were observed (Additional file 1: Table S3, sensitivity analysis 4). When excluding sepsis patients had any other diagnosis within 6 months or 1year before sepsis diagnosis, 78 and 64 significant associations remained (Additional file 1: Table S3, sensitivity analysis 5 and 6). When considering death as a competing risk, all 113 medical conditions identified in primary analysis were still significantly associated with sepsis (Additional file 1: Table S3, sensitivity analysis 7). Moreover, by using sepsis-free inpatients as unexposed controls (8,647 sepsis patients and 64,931 hospitalized sepsis-free controls), 96 out of 113 medical conditions also exhibited significantly elevated risks following sepsis (Additional file 1: Table S3, sensitivity analysis 8). Totally, 50 out of 113 significant medical conditions were identified in both primary and sensitivity analyses. As shown in Additional file 1: Fig. 10, majority of the disease trajectories identified in our study were replicated using either cSPADE algorithm or searching online Disease trajectory browser, some trajectories like “hypertensive disorders → depression” and “atrial fibrillation and flutter → acute renal failure” were detected in both analyses. 4. Discussion To our knowledge, this is among the first studies to comprehensively explore and visualize the disease trajectories following sepsis diagnosis, providing evidence for potential interventions targeting these diseases to prevent health declines among sepsis survivors. Using the population-based cohort UK Biobank, we found that, compared to individuals without sepsis, sepsis increased the risks of 113 subsequent medical conditions and formed 84 disease pairs with confirmed directionality. Three main clusters within the network were identified based on different disease systems affected by sepsis: circulatory, metabolic, and respiratory systems. The trajectories or leading causes of death after sepsis primarily originated from cardiometabolic, respiratory, neoplasm, digestive, and genitourinary system diseases. Furthermore, we also depicted disease trajectories and trajectories leading to the cause of death in different gender and age groups, and interestingly found that the networks were much more complex in male or younger individuals with sepsis. Confirming previous knowledge, we replicated the association between sepsis and pneumonia, chronic obstructive pulmonary disease (COPD) [ 8 ], AKI, CKD [ 5 ], heart failure [ 28 , 29 ], stroke [ 30 ], myocardial infarction [ 31 ], and also found several previous unknown diseases associated with sepsis, such as some digestive disorders or musculoskeletal system diseases. In line with previous investigations [ 32 , 33 ], we also observed infectious diseases, cancer, and cardiovascular diseases are major causes of death and readmission among the sepsis survivors. A series of sensitivity analyses demonstrated the reliability of our study, since PheWAS results were largely unchanged. Although online Danish Disease Trajectory Browser showed no trajectory directly from sepsis to subsequent diseases, trajectories derived from mediators like hypertension or diabetes were observed. In addition, some trajectories originated from sepsis to various following medical conditions were replicated using sequential pattern mining algorithm. Cardiometabolic diseases are one of the most significantly affected disease category in patients with sepsis [ 34 , 35 ], higher risks for many following cardiovascular diseases have been widely reported [ 36 ]. Infectious diseases, especially bacterial infection with sepsis showed the strongest association with following cardiovascular diseases [ 37 ]. After a median follow-up for 3 years, 10.4% sepsis survivors experienced a major cardiovascular event, of which hypertension and atrial fibrillation were associated with 34% and 46% enhanced risks for major cardiovascular events [ 38 ]. Consistent with previous investigations, we also observed that hypertension, ischemic heart disease and atrial fibrillation act as important mediators driving many subsequent diseases in sepsis patients, for example, the trajectories from atrial fibrillation to subsequent and acute renal failure, as well as the trajectory from ischemic heart disease to anemia. Importantly, hypertensive disorder not only drive many downstream disease trajectories but also link circulatory, respiratory, and neoplastic death in sepsis survivors. This indicates that blood pressure control is crucial for sepsis survivors to prevent a subsequent series of cardiometabolic adverse events. Consistent with previous findings [ 39 ], another major intermediary disease leading to a series of downstream trajectories is diabetes. The association between sepsis and subsequent diabetes remains significant even when excluding individuals with a history of any disease belonging to the metabolic system. Although some debates still exist in the association between diabetes and sepsis [ 40 ], previous study reported that infectious diseases increased risk of subsequent diabetes [ 39 ]. Evidence also supported that diabetes directly (association with other comorbidities) [ 41 ] or indirectly (release of metabolic biomarkers or inflammatory factors induced by infection)[ 42 ] influence subsequent cardiovascular diseases. One previous study reported that adult sepsis survivors with pre-existing diabetes increased risks of subsequent myocardial infarction and stroke [ 12 ]. Correspondingly, we also identified a trajectory originating from diabetes to hypertensive disorders, which subsequently affected a range of downstream conditions in the circulatory, musculoskeletal, and digestive systems. The possible biological mechanisms linking sepsis to cardiometabolic may attribute to release of inflammatory and metabolic factors, triggering a latent pro-thrombotic state or damaging the vascular endothelium after the infection [ 43 , 44 ]. In addition, in vivo experiments have revealed the potential molecular mechanisms involved [ 45 ], and their genetic correlation has also been identified between sepsis and cardiometabolic diseases [ 46 ]. As expected, we found sepsis associated with increased risk of following acute kidney failure, which have been widely confirmed in previous findings [ 47 – 49 ]. Herein, the mediated role of atrial fibrillation and asthma in sepsis survivors, linking the progression to acute renal failure with subsequent CKD, were also observed. Consistent with previous study reported that 61% sepsis patients progressed to AKI then 19% patients further developed to CKD after 1 year follow up, circulatory system diseases are risk factors involved in the development from sepsis to CKD [ 50 , 51 ]. Sepsis with following AKI is a complex clinical disorder, the possible mechanisms included inflammation and endothelial dysfunction, aberrant in blood flow and vascular permeability, coagulation imbalance, and renal inflammation attributed to hypertension and diabetes in septic patients [ 52 , 53 ]. We also observed sepsis impact subsequent incidence of respiratory and infectious system diseases, although few studies have focused on these associations. One previous report showed that higher systemic immune-inflammation index level was independently associated with an elevated likelihood of COPD [ 54 ]. In line with our study, COPD was reported to be an independent risk factor for mortality in patients with sepsis [ 55 ]. Although one previous Mendelian randomization reported nonsignificant causality from sepsis to asthma [ 56 ], the associations between sepsis and respiratory system diseases still need to be further explored. Previous studies have demonstrated that sepsis increases the risk of gastrointestinal bleeding [ 57 , 58 ], which might be one of the susceptibility factors for subsequent gastrointestinal infection [ 59 ]. It is inferred that immune system change and inflammatory factor release and microbiome alterations in lung [ 60 ] and gastrointestinal [ 61 ] caused by sepsis [ 62 ], as well as shared inflammatory pathways between them [ 63 – 65 ] may be the possible biological mechanism linking sepsis and these diseases. In subgroup analyses, trajectories leading to death are also much complex in male than in female, in line with previous study showing that males with sepsis may experience a less favorable outcome than females [ 66 ]. Many studies have reported that female had lower risk for following cardiometabolic diseases [ 67 ], respiratory system diseases [ 68 ] as compared to male. The underlying mechanisms for these differences may result from hormone [ 69 ] and unhealthy lifestyle in male [ 70 ]. An additional interesting finding from the subgroup analysis is that we observed much more complex disease trajectories, as well as trajectories leading to death, among younger sepsis patients compared to older individuals. Consistently, several previous research found more pronounced risk for cardiovascular events in younger sepsis patients [ 6 , 28 ]. These results indicated that sepsis might be an independent risk factor for subsequent many disease categories, as younger individuals were less influenced by baseline comorbidities or other risk factors, thus highlighting the importance of interventions in sepsis patients to prevent subsequent cardiovascular diseases [ 38 ]. The major strength of our study was the use of a largely population-based prospective cohort. We depicted the network of disease trajectories after sepsis and identified both commonly known and novel unknown trajectories. This visualized disease network enables us to holistically explore the development of diseases after sepsis, rather than focusing solely on the association between two separate diseases. For sepsis patients, prevention or intervention for mediators such as hypertension, diabetes, and atrial fibrillation is highly important, as they may drive many downstream health conditions. These findings may provide potential clues for further investigating the biological linkages within this trajectory network and promoting the overall health of sepsis survivors. There are also some limitations in our study. Firstly, sepsis is a heterogeneous disease; the signs and symptoms of sepsis are nonspecific, and there is no “gold standard” test for sepsis diagnosis [ 71 ]. Although many studies have used 3-digit ICD codes for sepsis identification, the insufficient accuracy in sepsis definition may have affected the research conclusions. Therefore, to illustrate the impact of sepsis heterogeneity on the outcome, a series of sensitivity analyses were performed. We found that the results were not largely changed after maintaining sepsis cases defined by primary ICD codes, excluding severe sepsis cases or excluding individuals with the same disease history as the medical condition. In addition, sepsis may be underestimated in UK Biobank due to the absence of explicit ICD-10 diagnosis codes despite documented coexistence of infection and organ dysfunction. Secondly, sepsis is usually caused by other diseases prior to sepsis, these preexisting diseases might influence trajectory after sepsis. To eliminate the impact of other diseases, we adjusted CCI at baseline considering the influence of comorbidities before sepsis, and the results largely remained significant. In addition, we further restricted septic patients who do not had any other diagnosis within 6 months or1 year prior to sepsis in sensitivity analysis and found most results still unchanged. Thirdly, some rare diseases with low incidence might have been neglected here due to the relatively limited follow-up period in the current study (median 3.99 years), which may influence observed results. Additional research with a longer follow-up is needed to explore subsequent diseases with later onset after sepsis. Fourthly, to further confirm the reliability of the results in our study, external validation of disease trajectories using other large-scale population-based cohorts is further needed. Fifthly, although some trajectories identified in our study were validated using sequential pattern mining algorithm or other online tools, the causal inference among these disease pairs has not been fully demonstrated. In addition, we mainly focused on the disease trajectories originating from sepsis; some disease pairs might have a bidirectional relationship that was not fully explored in our study. Sixthly, using the community-based UK Biobank, the matched sepsis-free controls might be much healthier than sepsis patients. To mitigate possible bias, we conducted sensitivity analysis by individually matching sepsis cases with up to 10 hospitalized sepsis-free controls from the UK Biobank, and found that sepsis was no longer significantly associated with subsequent diabetes, hypertensive disorders, or asthma. This may be attributable to the high prevalence of these common conditions among hospitalized patients. As more data become available, further in-depth investigation into the associations between sepsis and these diseases is needed. Lastly, we provide possible evidence for subsequent disease trajectories after the sepsis diagnosis, but the physiological or biological mechanisms among these disease pairs need to be further explored in the future. In conclusion, this community-based study showed a visualized network of disease trajectory, and demonstrated the increased risk for following cardiometabolic, respiratory, metabolic, and genitourinary system diseases or mortality after sepsis diagnosis. We identified hypertension, diabetes and atrial fibrillation act as an important mediator driving many following diseases or mortalities in sepsis patients, for example the following trajectories to ischemic heart disease and heat failure, as well as acute renal failure and CKD from these intermediated diseases in sepsis survivors. Furthermore, we also conducted comprehensive subgroup analyses for disease trajectories and leading causes of death in male and female, as well as young and old sepsis patients. Exploring potential interventions targeting these trajectories is of great importance, as it would prevent health declines and promote the quality of life for sepsis survivors. Abbreviations WHO: World Health Organization AKI: acute kidney injury COPD: chronic obstructive pulmonary disease SMR: Scottish Morbidity Record ICD: International Classification of Disease COVID-19: Coronavirus disease 2019 PheWAS: Phenome-wide association analysis HR: hazard ratio OR: odds ratio CCI: Charlson comorbidity index IQR: interquartile range CKD: chronic kidney disease Declarations Author information Authors and Affiliations Division of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China Med-X Center for Informatics, Sichuan University, Chengdu, 610065, China Chunyang Li, Chao Zhang, Zhiye Ying, Xiaoxi Zeng Department of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China. Bo Wang Department of Core Laboratory, Sichuan Provincial People's Hospital, University of Electronic Science and Technology of China, Chengdu, China Jie Chen, Wenyi Zhang Authors' contributions CYL and XXZ conceived of the idea, CYL and CZ performed analyses and wrote the manuscript, CZ prepared tables and figures, BW, JC and WYZ interpreted the results, ZYY prepared software and computing platform, XXZ supervised the study and revised the whole manuscript. Corresponding author Xiaoxi Zeng ( [email protected] ), Division of Nephrology, West China Biomedical Big Data Center, West China Hospital, Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan, 610041, China Ethics approval and consent to participate All the UK Biobank participants provided written informed consent before data collection. The UK Biobank has full ethical approval from the National Health Service (NHS) National Research Ethics Service (16/NW/0274). This study was approved by the Biomedical Research Ethics Committee of West China Hospital (2019–1171). Consent for publication Not applicable. Clinical trial number Not applicable. Availability of data and materials The data underlying this article can be applied from the UK Biobank (http://www.ukbiobank.ac.uk/register-apply). Competing interests The authors declare that they have no competing interests. Funding Key Research and Development Program of the Ministry of Science and Technology of the People's Republic of China (2022YFC2504501). Acknowledgments This research was conducted using the UK Biobank Resource under Application 54803. This work uses data provided by patients and collected by the National Health Service as part of their care and support. 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Unhealthy lifestyle behaviors, overweight, and obesity among childhood cancer survivors in the Netherlands: A DCCSS LATER study. Cancer. 2024;130(16):2856-72. Evans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med. 2021;47(11):1181-247. Additional Declarations No competing interests reported. Supplementary Files Additionalfile1methtablefig20250324v2.docx Additional file 1: Supplementary methods, tables and figures. Additionalfile2TableofICD10codes.xlsx Additional file 2: Table S1. Definition of combined ICD-10 codes. Cite Share Download PDF Status: Published Journal Publication published 14 Oct, 2025 Read the published version in BMC Infectious Diseases → Version 1 posted Editorial decision: Accepted 15 Sep, 2025 Editor assigned by journal 15 Sep, 2025 Reviews received at journal 28 Apr, 2025 Reviewers agreed at journal 08 Apr, 2025 Reviewers invited by journal 05 Apr, 2025 Submission checks completed at journal 31 Mar, 2025 First submitted to journal 27 Mar, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-5886414","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":440172349,"identity":"5a5174a9-5f8e-4d56-9e8e-ec07edfe88a4","order_by":0,"name":"Chunyang Li","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Chunyang","middleName":"","lastName":"Li","suffix":""},{"id":440172351,"identity":"44d12d82-0f86-45e8-be72-084205d1b0af","order_by":1,"name":"Chao Zhang","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Chao","middleName":"","lastName":"Zhang","suffix":""},{"id":440172359,"identity":"fab27e23-f261-4867-b2ee-b3a9bb352943","order_by":2,"name":"Bo Wang","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Bo","middleName":"","lastName":"Wang","suffix":""},{"id":440172361,"identity":"b3fe6fdc-f7fd-4dff-994e-1aae93b3c1a6","order_by":3,"name":"Jie Chen","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Jie","middleName":"","lastName":"Chen","suffix":""},{"id":440172362,"identity":"213d8457-409e-4a52-8887-7388883c2a30","order_by":4,"name":"Wenyi Zhang","email":"","orcid":"","institution":"University of Electronic Science and Technology of China","correspondingAuthor":false,"prefix":"","firstName":"Wenyi","middleName":"","lastName":"Zhang","suffix":""},{"id":440172363,"identity":"c046b452-f1e8-4b9b-a4af-d2faee5da726","order_by":5,"name":"Zhiye Ying","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Zhiye","middleName":"","lastName":"Ying","suffix":""},{"id":440172364,"identity":"cb71a35d-519c-4953-9f31-0e1b5d7440c2","order_by":6,"name":"Huazhen Yang","email":"","orcid":"","institution":"Sichuan University","correspondingAuthor":false,"prefix":"","firstName":"Huazhen","middleName":"","lastName":"Yang","suffix":""},{"id":440172366,"identity":"cb205846-0c98-4492-a727-01aa26f9e2b8","order_by":7,"name":"Xiaoxi Zeng","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA3UlEQVRIie3PMQrCMBTG8ZYHmZ7OcTFXeFJocfEsFcHJwV2oglAXi6vewkkcAxm6iB7ARRC66FAQhIKD0dEhtZtg/pDt+xGe49hsPxvpByAdd1KNsLAKeYf0HRGzRF1w2GlSindebCNBErKTibjzfb+9pJ7XmNY2jWSnWmvJAjIR4AOfcoLuAmqboxvLkCQybiJMXH0KadyNATNNonKCHL1TTkr/gkwTKCccB767pFTfwoIiiVVrpZhvJGK28274GDXpoM5UxJGop9PMSF7nfAygZP+a5OUbm81m++ueJ89B6AuDvlYAAAAASUVORK5CYII=","orcid":"","institution":"Sichuan University","correspondingAuthor":true,"prefix":"","firstName":"Xiaoxi","middleName":"","lastName":"Zeng","suffix":""}],"badges":[],"createdAt":"2025-01-23 08:23:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5886414/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5886414/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1186/s12879-025-11701-z","type":"published","date":"2025-10-14T15:58:39+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":80285581,"identity":"2b6d31ae-edd7-4308-958b-bb9c6dd35dc2","added_by":"auto","created_at":"2025-04-10 06:44:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44700,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eFlowchart of the selection of study population. \u003c/strong\u003eAfter a series of inclusion and exclusion criteria, 8647 septic patients with 86386 matched controls were included for analysis.\u003c/p\u003e","description":"","filename":"fig1.png","url":"https://assets-eu.researchsquare.com/files/rs-5886414/v1/d9411c4bdfcf3e50a853bcaf.png"},{"id":80285582,"identity":"741eec73-de44-4345-a01c-3de4eb33eb06","added_by":"auto","created_at":"2025-04-10 06:44:00","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":187933,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eHazard ratios (HRs) of 113 significant medical conditions among sepsis patients. \u003c/strong\u003eThe X axis with different colors represents disease categories of ICD-10 codes from A to N. The Y axis shows the significant HRs after selection.\u003c/p\u003e","description":"","filename":"fig2.png","url":"https://assets-eu.researchsquare.com/files/rs-5886414/v1/0d915f630fdf1c9efdf454b4.png"},{"id":80284809,"identity":"5abf5133-368a-468d-bb17-3ccbca178774","added_by":"auto","created_at":"2025-04-10 06:36:00","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":974941,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDisease trajectories in A) circulatory, B) metabolic, C) respiratory and D) genitourinary system diseases originated from sepsis. \u003c/strong\u003eWithin the circle, the combined ICD-10 codes of medical conditions were presented. The color of the circle from light to dark indicates the hazard ratio (HR) between this disease pair ranging from low to high. The color of the edge connecting two diseases, ranging from light to dark, indicates the magnitude of the odds ratio (OR). The number above the arrow corresponds to the number of sepsis patients with temporal order from disease 1 to disease 2.\u003c/p\u003e","description":"","filename":"fig3.png","url":"https://assets-eu.researchsquare.com/files/rs-5886414/v1/2e2273e70b0141fb68cb03ff.png"},{"id":80284806,"identity":"a859b2f9-0767-48ab-ad08-cbc9584572eb","added_by":"auto","created_at":"2025-04-10 06:36:00","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":86886,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eTrajectories lead to mortality in A) circulatory, B) neoplasm and C) respiratory system diseases among sepsis.\u003c/strong\u003eThe combined ICD-10 codes of medical conditions were presented in the circle. The color of the circle from light to dark indicates the hazard ratio (HRs) between this disease pair ranging from low to high. The color of the edge connecting two diseases, ranging from light to dark, indicates the magnitude of the odds ratio (OR). The number positioned above the arrow corresponds to the count of sepsis patients following the temporal order from disease 1 to disease 2, ultimately leading to death.\u003c/p\u003e","description":"","filename":"fig4.png","url":"https://assets-eu.researchsquare.com/files/rs-5886414/v1/cb97e156f30f81f960ede703.png"},{"id":93956095,"identity":"528ae2dd-76fe-4c18-95e5-9f654cf9f2c7","added_by":"auto","created_at":"2025-10-20 16:10:27","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2603815,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5886414/v1/5211cba2-3897-445f-a2cf-ce1886070c78.pdf"},{"id":80285584,"identity":"a327ddd1-5285-4c57-b9ef-6c9efbd6b206","added_by":"auto","created_at":"2025-04-10 06:44:01","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":4521896,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 1: Supplementary methods, tables and figures.\u003c/p\u003e","description":"","filename":"Additionalfile1methtablefig20250324v2.docx","url":"https://assets-eu.researchsquare.com/files/rs-5886414/v1/0b1d34e3478e3572d7e388f0.docx"},{"id":80284820,"identity":"e9e4caa8-a725-4dad-a62c-91910c6d03fc","added_by":"auto","created_at":"2025-04-10 06:36:01","extension":"xlsx","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":62679,"visible":true,"origin":"","legend":"\u003cp\u003eAdditional file 2: Table S1. Definition of combined ICD-10 codes.\u003c/p\u003e","description":"","filename":"Additionalfile2TableofICD10codes.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-5886414/v1/8cec8a65cc7bf6cb4a5829d5.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Disease trajectory and mortality among sepsis patients: a prospective cohort study","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eSepsis is a complex and life-threatening medical condition, defined as life-threatening organ dysfunction caused by a dysregulated host response to infection [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to estimates by the World Health Organization (WHO), there were approximately 48.9\u0026nbsp;million cases of sepsis and 11\u0026nbsp;million (almost 20%) deaths related to sepsis worldwide in 2020 [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e], sepsis is the most common cause of in-hospital deaths [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePatients who survive sepsis often have long-term physical and psychological declines as well as cognitive disabilities, which can significantly impact their quality of life [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Sepsis has been widely reported to increase the risks of subsequent acute kidney injury (AKI) [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], myocardial infarction, stroke [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e], transient ischemic attack [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e], pneumonia and chronic obstructive pulmonary disease (COPD) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In addition to somatic disease, sepsis also leads to persistent cognitive impairment and functional disability [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], for example dementia [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e], anxiety, depression and posttraumatic stress disorder [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Diabetes and hypertension may affect following cardiovascular disease in sepsis survivors through either direct or indirect association with other comorbidities or change inflammation cascade [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. However, previous studies mainly focused on separate diseases, and no comprehensive investigation about the cross-talks of the disease conditions derived from sepsis has been conducted. Importantly, the subsequent diseases caused by sepsis may have a temporal order and form a complex disease network influencing each other. Therefore, to prevent future health declines and improve quality of life in septic patients, it is important to comprehensively understand the disease trajectories and networks of sepsis.\u003c/p\u003e \u003cp\u003eRecently, disease trajectory analysis has been widely used to investigate the cross-talks among depression [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e], anxiety and stress-related disorders [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e], nonalcoholic fatty liver disease [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] and breast cancer [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. By visualizing possible emerged diseases with their temporal order and depicting the disease-disease association with magnitude, one can examine the sequential patterns and causal relationships after a certain disease origin. By using population-based UK Biobank, we mainly aimed to investigate the subsequent medical conditions and disease trajectory network with temporal order after prior diagnosis of sepsis. We further explored and compared trajectories following sepsis diagnosis separately in different gender and age groups.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design\u003c/h2\u003e \u003cp\u003eIn brief, UK Biobank is an ongoing population-based cohort recruited approximately 500,000 participants from England, Scotland and Wales, aged 40\u0026ndash;69 at the time of recruitment between 2006 and 2010. Baseline data about participants\u0026rsquo; demographic characteristics, socioeconomic status, behavioral, lifestyle factors, medical history and medications were collected in 22 research assessment centers across the UK at the recruitment. Follow-up is conducted chiefly through linkages to Hospital Episode Statistics database for England, the Scottish Morbidity Record (SMR) for Scotland and Patient Episode Database for Wales. Death registry records were accessed by linking NHS for England and Wales, as well as NHS Central Register, National Records of Scotland [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn the present study, based on the UK Biobank data, among the 502,507 participants, we first excluded 194 individuals who withdrew their informed consent forms, 623 participants with missing values for Townsend deprivation index, sex and birth year. Then, among the remaining 501,690 participants, individuals that were diagnosed with sepsis before December 31, 2019 were included in the exposed cohort. The diagnosis was ascertained by inpatient records. To preclude the potential reverse association between sepsis and other acute disorders, patients with any other diseases diagnosed within 1 month prior to the sepsis diagnosis were excluded. For chronic conditions, we hypothesized that patients underwent comprehensive examinations during their hospital stay, thus, diseases not presented within 1 month before sepsis diagnosis shall be ruled out as pre-existing disorders and could be treated as candidates for further trajectory analysis. We then randomly selected up to 10 sepsis-free controls from UK Biobank, and individually matched to the sepsis patient by birth year, sex, and Townsend deprivation index (transformed to tertiles) [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Finally, 95,033 individuals were included in the study cohort, including 8,647 septic patients and 86,386 matched sepsis-free controls (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The characteristics of sepsis and unexposed control groups at the index date were summarized and compared. Continuous variables were presented as median with interquartile range (IQR) and compared using analysis of variance (ANOVA) tests, and categorical variables were reported as number with percentage and compared by Chi-square tests.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFollow-up for all participants started from 1 month after the index date, and until death, loss to follow-up or the end of study (December 31, 2019), whichever occurred first. For the exposed sepsis patients, the index date was designated as the date for sepsis diagnosis, and the index date of unexposed controls were the same as their matched sepsis patients. The flow chart for inclusion and exclusion of participants was presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Ascertainment of sepsis\u003c/h2\u003e \u003cp\u003eSepsis patients were defined as individuals with a first-time hospitalization for sepsis (primary or secondary diagnosis), using explicit diagnosis ICD-10 codes (provided in Additional file 1: Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. In order to eliminate the potential influence of the Coronavirus Disease 2019 (COVID-19) on disease occurrence, sepsis inpatients were included from January 1, 1997 and December 31, 2019 [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. For sepsis patients, the index date was defined as the date of the first sepsis diagnosis. For the unexposed controls who were matched to sepsis patients, the index date was the same as the date of sepsis diagnosis for their matched cases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Diagnoses of other medical conditions\u003c/h2\u003e \u003cp\u003eDiagnoses of other medical conditions were retrieved from the main and secondary diagnoses in the UK Biobank inpatient hospital, ICD-9 codes were transformed to ICD-10 codes [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Diagnoses related to pregnancy, childbirth, perinatal conditions, and unclassified symptoms or signs were excluded, and analyses were restricted to Chaps.\u0026nbsp;1\u0026ndash;14 of ICD-10 codes. We used the 3-digit ICD-10 codes and combined conditions with clinical or biological similarities for medical condition identification (Additional file 2: Table of ICD-10 codes) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Thus, a total of 469 medical conditions were examined in the trajectory analysis. If multiple records were identified for the same individual, only the first record and corresponding date were maintained for analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Death\u003c/h2\u003e \u003cp\u003eThe cause of death was recorded in the death registry in UK Biobank with ICD-10 codes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. based on Chaps.\u0026nbsp;1\u0026ndash;14 of ICD-10 codes, the causes of death were classified into 13 categories, namely death due to infectious diseases, neoplasms, hematopoietic diseases, endocrine/metabolic disease, mental disorders, diseases of the nervous system, diseases of sense organs, circulatory system diseases, respiratory system diseases, digestive system diseases, dermatologic diseases, musculoskeletal system diseases, and genitourinary system diseases (codes were listed in Additional file 2: Table of ICD-10 codes).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Trajectory analyses of sepsis\u003c/h2\u003e \u003cp\u003eDisease trajectory following sepsis was constructed using three interrelated steps which have been previously described [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. Firstly, Phenome-wide association analysis (PheWAS) using conditional Cox regression was performed to investigate the risks of 469 medical conditions among participants with sepsis compared to their matched unexposed individuals. Follow-up of the individuals in the sub-cohort started from 1 month after sepsis diagnosis, and ended on date of the medical condition, date of death or December 31, 2019, whichever came first. To identification of increased risks of medical conditions after sepsis, diseases occurred in at least 100 sepsis patients, with a p value less than the Bonferroni correction threshold (0.05/469\u0026thinsp;=\u0026thinsp;0.000106) and hazard ratio (HR) greater than 1, were considered in the second step of the analysis. In the second step, we conducted binomial tests to explore whether the pattern of a later diagnosis (D2) occurring after another previous diagnosis (D1) was dominant, or the proportion of patients bearing this pattern exceeded 50%, among those with both D1 and D2 diagnoses. These tests were performed on all possible D1 and D2 pairs with a temporal order that occurred in at least 100 sepsis patients. A binomial test p value\u0026thinsp;\u0026lt;\u0026thinsp;Bonferroni correction threshold was included in the next step. Thirdly, nested case-control study using conditional logistic regression was performed to evaluate the association between D1 and D2. Disease pairs passing Bonferroni corrected threshold with odds ratio (OR)\u0026thinsp;\u0026gt;\u0026thinsp;1, and occurred in at least 50 sepsis patients had D1 and D2 diagnosis after sepsis diagnosis were considered as significant (details were listed in Additional file 1: Methods).\u003c/p\u003e \u003cp\u003eSimilar methods using the three interrelated steps as described above were conducted to investigate the trajectory leading to death in sepsis survivors, with restricting D1\u0026rarr;D2\u0026rarr;Death pairs occurred in at least 25 sepsis patients for binomial test and 10 sepsis patients for nested case-control study (see in Additional file 1: Methods).\u003c/p\u003e \u003cp\u003eThe trajectory network was formed by combining disease pairs with overlapping diseases. For example, disease pairs D1\u0026rarr;D2 and D2\u0026rarr;D3, with overlapping D2, were combined with the trajectory D1\u0026rarr;D2\u0026rarr;D3. The flowchart of disease trajectories selection following sepsis diagnosis and trajectories selection were shown in Additional file 1: Figs.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e and \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Subgroup analysis\u003c/h2\u003e \u003cp\u003eTo investigate whether the disease trajectory of sepsis was differed by gender and age at index date, the disease trajectory after sepsis was analyzed separately in these subgroups. Herein, we divided sepsis patients into two different age groups according to the median age (which is 66.96), then sepsis patients aged\u0026thinsp;\u0026le;\u0026thinsp;66.96 together with their matched controls were categorized into younger group while sepsis patients aged\u0026thinsp;\u0026gt;\u0026thinsp;66.96 and their matched controls were grouped as older group. By using the same analyses for disease trajectory and trajectory leading to death, we adjusted the threshold for disease trajectory analysis for reduced sample size in subgroup analysis (temporary order of D1\u0026rarr;D2 pairs occurred in at least 50 sepsis patients, or D1\u0026rarr;D2\u0026rarr;Death pairs occurred in at least 10 sepsis patients). The flowchart of sex- ang age- specific disease trajectories and trajectories leading to death after sepsis were shown in Additional file 1: Figs.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and \u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, respectively.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eIn order to test the robustness of diseases associated with sepsis identified in our study, we repeated PheWAS analyses by 1) excluding individuals with a history of any disease belonged to the same category of the disease outcomes. For example, to explore the association between sepsis and following hypertension, individuals with any cardiovascular disease before index date were excluded; 2) additionally adjusting Charlson comorbidity index (CCI) on the index date to control the impact of comorbidities at baseline (definition of CCI was listed in Additional file 1: Table \u003cspan refid=\"MOESM2\" class=\"InternalRef\"\u003eS2\u003c/span\u003e) [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]; 3) maintaining sepsis patients by defining sepsis with primary diagnosis in UK Biobank; 4) excluding sepsis patients with critical care records at the index episode (these patients were identified as severe sepsis patients), to eliminate the influence of sepsis heterogeneity on the subsequent risks of medical conditions. 5) excluding sepsis patients had any other diseases diagnosed within 6 months or 1 year prior to the sepsis diagnosis. 6) the subdistribution HR of 469 following medical conditions were estimated using Fine-Gray competing risk model, with death treated as the competing risk. 7) sepsis patients were individually matched to hospitalized sepsis-free controls from UK Biobank based on the same birth year, year of sepsis diagnosis (for inpatient controls this was defined as the year at the first inpatient admission), sex, gender, and Townsend deprivation index (in tertiles).\u003c/p\u003e \u003cp\u003eTo further validate the confidence of disease trajectories in our study, following sensitivity analyses were performed: 1) identification of trajectory using sequential pattern mining with cSPADE algorithm [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], which could find sequential patterns (for example, the disease trajectory of \u0026ldquo;sepsis\u0026rarr;hypertension\u0026rdquo;) with higher frequency of incidence as compared to all the patterns derived from dataset. We included sequential disease patterns occurred in at least 1% sepsis patients to detect patterns with rare frequency [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]; 2) searching Danish Disease Trajectory Browser (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dtb.cpr.ku.dk/\u003c/span\u003e\u003cspan address=\"http://dtb.cpr.ku.dk/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) using corresponding ICD-10 codes identified in our study, the threshold set herein were length\u0026thinsp;\u0026ge;\u0026thinsp;2, relative risk\u0026thinsp;\u0026ge;\u0026thinsp;1 and number of patients\u0026thinsp;\u0026ge;\u0026thinsp;20. This online platform could visualize disease trajectories in browser using Danish National Patient Registry, comprising 7.2\u0026nbsp;million patients and 122\u0026nbsp;million admissions with ICD-10 codes recorded [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Baseline characteristics\u003c/h2\u003e \u003cp\u003eOf the 8,647 sepsis patients and their 86,386 matched individuals, median age was 66.92 on the index date, and most of them were male (50.63%). As shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the age, sex, and Townsend deprivation score was appropriately balanced for the exposed cohort and the matched cohort.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCharacteristics of the sepsis patients and their matched individuals without sepsis.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTotal (n\u0026thinsp;=\u0026thinsp;95,033)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSepsis patients (n\u0026thinsp;=\u0026thinsp;8,647)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eMatched individuals without sepsis (n\u0026thinsp;=\u0026thinsp;86,386)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge at index (median with IQR), years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e66.92 (58.25\u0026ndash;72.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e66.96 (58.25\u0026ndash;72.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e66.91 (58.24\u0026ndash;72.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.92\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eFemale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e46917 (49.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4268 (49.36%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e42649 (49.37%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMale\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48116 (50.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4379 (50.64%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e43737 (50.63%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTownsend deprivation index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.99\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eHigh\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e38980 (41.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3548 (41.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e35432 (41.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eMedium\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e30009 (31.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2730 (31.57%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e27279 (31.58%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003eLow\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e26044 (27.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2369 (27.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e23675 (27.41%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFollow-up time (median with IQR), years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.99 (1.79\u0026ndash;8.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.12 (1.21\u0026ndash;8.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.08 (1.84\u0026ndash;8.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCharlson comorbidity index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e0\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e70583 (74.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3201 (37.02%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e67382 (78.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e1\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12934 (13.61%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2379 (27.51%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10555 (12.22%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e5854 (6.16%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e922 (10.66%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4932 (5.71%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3673 (3.86%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1252 (14.48%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2421 (2.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cem\u003e\u0026gt;\u0026thinsp;3\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1989 (2.09%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e893 (10.33%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1096 (1.27%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eANOVA test was performed for continuous variables.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eχ\u003csup\u003e2\u003c/sup\u003e test was performed for category variables.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Medical conditions and causes of death associated with sepsis\u003c/h2\u003e \u003cp\u003eThe median follow-up time for the whole cohort was 3.99 years [interquartile range (IQR), 1.79\u0026ndash;8.81]. As expected, among 469 medical conditions, sepsis was associated with more frequent occurrences of 113 diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Additional file 1: Table S3), including genitourinary system diseases (i.e., chronic kidney disease (CKD) (HR 3.83, 95%CI 3.45\u0026ndash;4.26, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)), metabolic system disease (i.e., diabetes (HR 1.63, 95%CI 1.45\u0026ndash;1.84, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)), circulatory system disease (i.e., hypertensive disorder (HR 1.60, 95%CI 1.48\u0026ndash;1.73, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)) and respiratory system disease (i.e., COPD (HR 3.38, 95%CI 3.02\u0026ndash;3.79 P\u0026thinsp;\u0026lt;\u0026thinsp;0.001)), \u003cem\u003eet al\u003c/em\u003e. We also observed an association between sepsis and subsequent diagnosis of several infectious diseases. For instance, sepsis was associated with bacterial intestinal infections (HR 6.43, 95%CI 5.11\u0026ndash;8.09, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and infectious gastroenteritis and colitis (HR 5.19, 95%CI 4.69\u0026ndash;5.74, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), with a median interval of follow-up of 3.9 years after the diagnosis of sepsis. In addition to somatic diseases, sepsis also had significant impact on subsequent mental disorders, such as alcohol abuse (HR 2.21, 95% CI 1.80\u0026ndash;2.71, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), tobacco abuse (HR 1.93, 95% CI 1.70\u0026ndash;2.19, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), depression (HR 2.49, 95% CI 2.24\u0026ndash;2.78, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001), and anxiety (HR 2.27, 95% CI 2.01\u0026ndash;2.56, P\u0026thinsp;\u0026lt;\u0026thinsp;0.001).\u003c/p\u003e \u003cp\u003eFor the cause-specific mortality, except disease of sense organ (N \u003csub\u003edeath\u003c/sub\u003e=0), sepsis was significantly associated with increased risk of deaths due to majority categories of diseases, with the exception of musculoskeletal system diseases and hematopoietic diseases (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eThe association between sepsis and causes of death.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCauses of death\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eTotal (N\u0026thinsp;=\u0026thinsp;95033)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eFemale (N\u0026thinsp;=\u0026thinsp;46917)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eMale (N\u0026thinsp;=\u0026thinsp;48116)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eYounger individuals (N\u0026thinsp;=\u0026thinsp;47562)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c11\" namest=\"c10\"\u003e \u003cp\u003eOlder individuals (N\u0026thinsp;=\u0026thinsp;47471)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNumber \u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber \u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eNumber \u003csup\u003ec\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eNumber \u003csup\u003ed\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eNumber \u003csup\u003ee\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eHR (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInfectious disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e181\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e31.47 (23.32\u0026ndash;42.47) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e74\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e52.11 (29.43\u0026ndash;92.27) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e24.62 (17.23\u0026ndash;35.18) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e30.56 (19.30\u0026ndash;48.40) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e32.15 (21.65\u0026ndash;47.78) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNeoplasms\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8.86 (8.17\u0026ndash;9.61) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e472\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.84 (8.67\u0026ndash;11.18) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e8.26 (7.43\u0026ndash;9.17) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e7.44 (6.60\u0026ndash;8.40) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e653\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10.26 (9.19\u0026ndash;11.46) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEndocrine/metabolic disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11.53 (9.63\u0026ndash;13.81) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e95\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.89 (12.75\u0026ndash;25.09) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e9.55 (7.69\u0026ndash;11.85) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e12.75 (9.74\u0026ndash;16.71) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e137\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e10.62 (8.33\u0026ndash;13.53) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMental disorders\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.32 (8.35\u0026ndash;12.76) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.33 (6.53\u0026ndash;13.33) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.90 (8.37\u0026ndash;14.21) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e5.09 (3.36\u0026ndash;7.72)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.64 (10.57\u0026ndash;17.60) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiseases of the nervous system\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9.94 (8.12\u0026ndash;12.18) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e8.01 (5.68\u0026ndash;11.30) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e11.17 (8.68\u0026ndash;14.38) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.49 (4.55\u0026ndash;9.26) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e139\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e12.37 (9.62\u0026ndash;15.91) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCirculatory system disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e704\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.99 (7.23\u0026ndash;8.83) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10.91 (9.05\u0026ndash;13.15) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e471\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e7.05 (6.26\u0026ndash;7.94) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e6.61 (5.64\u0026ndash;7.74) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e444\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e9.10 (8.00-10.36) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRespiratory system disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e670\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.99 (9.84\u0026ndash;12.28) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.53 (11.07\u0026ndash;16.53) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e442\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e10.01 (8.76\u0026ndash;11.44) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e233\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e9.89 (8.24\u0026ndash;11.88) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e11.68 (10.16\u0026ndash;13.43) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigestive system disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e243\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15.66 (12.78\u0026ndash;19.17) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.20 (13.41\u0026ndash;27.51) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e14.17 (11.07\u0026ndash;18.12) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e17.74 (13.19\u0026ndash;23.86) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e120\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e13.97 (10.57\u0026ndash;18.46) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMusculoskeletal system disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16.36 (11.36\u0026ndash;23.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e23.30 (12.75\u0026ndash;42.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e13.01 (8.18\u0026ndash;20.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e15.57 (8.16\u0026ndash;26.69)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e16.74 (10.76\u0026ndash;26.05) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGenitourinary system disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e234\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17.55 (14.15\u0026ndash;21.76) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.72 (16.42\u0026ndash;37.23) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e15.19 (11.78\u0026ndash;19.60) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e94\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e24.67 (16.86\u0026ndash;36.10) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e14.69 (11.29\u0026ndash;19.12) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHematopoietic disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e47\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.95 (11.59\u0026ndash;30.99)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e26.17 (10.24\u0026ndash;66.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e16.56 (9.26\u0026ndash;29.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e26.66 (11.86\u0026ndash;59.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e15.10 (8.06\u0026ndash;28.30)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDisease of sense organ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003eNA\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDermatologic disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30.35 (14.88\u0026ndash;61.94)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.34 (10.65\u0026ndash;80.79)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e16\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e31.37 (11.49\u0026ndash;85.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e58.69 (13.13-262.29)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e \u003cp\u003e23.26 (10.18\u0026ndash;53.17)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003e*\u003c/sup\u003e Association with HR\u0026thinsp;\u0026gt;\u0026thinsp;1, p value\u0026thinsp;\u0026lt;\u0026thinsp;the Bonferroni correction (0.05/13\u0026thinsp;=\u0026thinsp;0.00384615) and occurred in at least 100 sepsis patients (50 for female or male patients, and 50 for younger or older patients), was considered as significant association between sepsis and causes of death as compared to individuals without sepsis.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ea\u003c/sup\u003e Number of septic patients who died due to the causes of death, including 8647 sepsis patients and 86386 unexposed controls.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003eb\u003c/sup\u003e Number of female septic patients who died due to the causes of death, including 4268 sepsis patients and 42649 unexposed controls.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ec\u003c/sup\u003e Number of male septic patients who died due to the causes of death, including 4379 sepsis patients and 43737 unexposed controls.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ed\u003c/sup\u003e Number of younger septic patients (aged\u0026thinsp;\u0026le;\u0026thinsp;66.96) who died due to the causes of death, including 4325 sepsis patients and 43237 unexposed controls.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e\u003csup\u003ee\u003c/sup\u003e Number of older septic patients (aged\u0026thinsp;\u0026gt;\u0026thinsp;66.96) who died due to the causes of death, including 4322 sepsis patients and 43149 unexposed controls.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Temporary disease trajectories in sepsis patients\u003c/h2\u003e \u003cp\u003eAmong 113 medical conditions associated with sepsis, 130 pairs were identified (Additional file 1: Table S4). Among the disease pairs with temporary order, four major patterns were identified (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In Cluster 1 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eA), the intermediate diseases after sepsis mediating most of downstream diseases were hypertensive disorders, atrial fibrillation and flutter, and ischemic heart disease, then these circulatory system diseases further led to other diseases like heart failure, pneumonia and acute renal failure with subsequent CKD. Similarly, in Cluster 2 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eB), the intermediate disease was diabetes, further resulted in hypertensive disorders with subsequent obesity, cardiac arrhythmias and cerebrovascular diseases. In Cluster 3 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eC), conditions such as urinary system infection and acute renal failure were the most prevalent following sepsis, and a trajectory from acute renal failure to CKD was observed after sepsis. Cluster 4 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eD) mainly included respiratory system disease (i.e. asthma or COPD) after sepsis, further associated with following musculoskeletal system disease including osteoarthritis and joint disorders. The overview of other disease trajectories after sepsis was presented in Additional file 1: Fig.\u0026nbsp;5.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Temporary disease trajectories leading to death following sepsis\u003c/h2\u003e \u003cp\u003eTotally, based on 13 D1\u0026rarr;D2\u0026rarr;Death pairs were identified (Additional file 1: Table S5), we identified three major networks linking sepsis to death due to neoplasms, circulatory system diseases and respiratory system diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Hypertensive diseases occurred after sepsis, which then led to heart failure, atrial fibrillation and flutter as well as acute renal failure, eventually causing death due to circulatory system diseases (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eA). Sepsis caused acute renal failure and metastatic cancer, which eventually resulting in death due to neoplasms (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eB). Additionally, sepsis triggered hypertensive diseases and COPD, which linked to subsequent pneumonia and respiratory failure, culminating in death due to respiratory system disease (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003eC)..\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Subgroup analyses\u003c/h2\u003e \u003cp\u003eCharacteristics of female and male individuals were listed in Additional file 1: Table S6. Medical conditions associated with prior sepsis using PheWAS analysis in female and male were largely similar, but sepsis increased subsequent risks of some diseases including chronic rheumatic heart disease and stroke were only observed in male (Additional file 1: Table S7). More disease trajectories after sepsis in male were observed that there were totally 82 and 106 disease pairs in female and male, respectively (Additional file 1: Table S8). Both female and male sepsis patients were associated with an increased risk of subsequent diabetes, hypertensive disorders, COPD and asthma (Additional file 1: Fig.\u0026nbsp;6). However, the trajectory from sepsis to ischemic heart disease in females was primarily mediated by hypertensive disorders, whereas the progression in males was mainly mediated by diabetes. Additionally, ischemic heart disease in males was associated with a significantly greater number of downstream medical conditions compared to that in females. Conversely, certain trajectories following sepsis, such as the progression from acute renal failure to CKD was only observed in female survivors. In female sepsis patients, only one trajectory leading to death was observed, which progressed from hypertensive disorders to disorders of fluid, electrolyte, and acid-base balance, ultimately culminating in circulatory death. For male patients, two major causes of death were identified, such as circulatory death attributed to a trajectory from hypertensive disorders to respiratory failure, and respiratory death due to a trajectory from heart failure to respiratory failure (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Additional file 1: Table S9 and Fig.\u0026nbsp;7).\u003c/p\u003e \u003cp\u003eCharacteristics of young and old participants were listed in Additional file 1: Table S6. More disease trajectories were observed in younger sepsis patients (aged\u0026thinsp;\u0026le;\u0026thinsp;66.96 years) compared to older patients. Disease trajectories originating from hypertension after sepsis, followed by depression and anxiety diagnosis, were only observed in younger patients (Additional file 1: Fig.\u0026nbsp;8 and Table S10 and Table S11). The risk of death for younger sepsis patients increased across 8 different categories of causes, whereas for older sepsis patients, the mortality risk increased across 10 categories (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Interestingly, a greater number of trajectories leading to death were observed among younger sepsis patients. For instance, in younger sepsis patients, sepsis followed by COPD led to respiratory failure, ultimately resulting in respiratory death. In addition, sepsis followed by hypertensive disorders also led to acute renal failure and subsequent circulatory death (see Additional file 1: Fig.\u0026nbsp;9 and Table S12). In contrast, for older sepsis patients, the trajectory to respiratory system death was from pneumonia to respiratory failure (see Additional file 1: Fig.\u0026nbsp;9 and Table S12).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Sensitivity analyses\u003c/h2\u003e \u003cp\u003eAs shown in Additional file 1: Table S3 (sensitivity analysis 1 and 2), most of the PheWAS results remained largely unchanged when excluding individuals with a history of any disease belonged to the same category of the disease outcomes or additionally adjusting for CCI at baseline. When maintaining sepsis patients defined by primary diagnosis codes in UK Biobank (totally 40682 participants, of which 3702 sepsis patients and 36980 matched individuals without sepsis), 102 associations between sepsis and subsequent medical conditions remained significant (Additional file 1: Table S3, sensitivity analysis 3). After excluding sepsis patients who had critical care records (severe cases), 87679 individuals (7978 sepsis patients and 79701 their matched controls) left, and 107 significant associations between sepsis and subsequent medical conditions were observed (Additional file 1: Table S3, sensitivity analysis 4). When excluding sepsis patients had any other diagnosis within 6 months or 1year before sepsis diagnosis, 78 and 64 significant associations remained (Additional file 1: Table S3, sensitivity analysis 5 and 6). When considering death as a competing risk, all 113 medical conditions identified in primary analysis were still significantly associated with sepsis (Additional file 1: Table S3, sensitivity analysis 7). Moreover, by using sepsis-free inpatients as unexposed controls (8,647 sepsis patients and 64,931 hospitalized sepsis-free controls), 96 out of 113 medical conditions also exhibited significantly elevated risks following sepsis (Additional file 1: Table S3, sensitivity analysis 8). Totally, 50 out of 113 significant medical conditions were identified in both primary and sensitivity analyses. As shown in Additional file 1: Fig.\u0026nbsp;10, majority of the disease trajectories identified in our study were replicated using either cSPADE algorithm or searching online Disease trajectory browser, some trajectories like \u0026ldquo;hypertensive disorders \u0026rarr; depression\u0026rdquo; and \u0026ldquo;atrial fibrillation and flutter \u0026rarr; acute renal failure\u0026rdquo; were detected in both analyses.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eTo our knowledge, this is among the first studies to comprehensively explore and visualize the disease trajectories following sepsis diagnosis, providing evidence for potential interventions targeting these diseases to prevent health declines among sepsis survivors. Using the population-based cohort UK Biobank, we found that, compared to individuals without sepsis, sepsis increased the risks of 113 subsequent medical conditions and formed 84 disease pairs with confirmed directionality. Three main clusters within the network were identified based on different disease systems affected by sepsis: circulatory, metabolic, and respiratory systems. The trajectories or leading causes of death after sepsis primarily originated from cardiometabolic, respiratory, neoplasm, digestive, and genitourinary system diseases. Furthermore, we also depicted disease trajectories and trajectories leading to the cause of death in different gender and age groups, and interestingly found that the networks were much more complex in male or younger individuals with sepsis.\u003c/p\u003e \u003cp\u003eConfirming previous knowledge, we replicated the association between sepsis and pneumonia, chronic obstructive pulmonary disease (COPD) [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e], AKI, CKD [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e], heart failure [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e], stroke [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e], myocardial infarction [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e], and also found several previous unknown diseases associated with sepsis, such as some digestive disorders or musculoskeletal system diseases. In line with previous investigations [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e], we also observed infectious diseases, cancer, and cardiovascular diseases are major causes of death and readmission among the sepsis survivors. A series of sensitivity analyses demonstrated the reliability of our study, since PheWAS results were largely unchanged. Although online Danish Disease Trajectory Browser showed no trajectory directly from sepsis to subsequent diseases, trajectories derived from mediators like hypertension or diabetes were observed. In addition, some trajectories originated from sepsis to various following medical conditions were replicated using sequential pattern mining algorithm.\u003c/p\u003e \u003cp\u003eCardiometabolic diseases are one of the most significantly affected disease category in patients with sepsis [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e], higher risks for many following cardiovascular diseases have been widely reported [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Infectious diseases, especially bacterial infection with sepsis showed the strongest association with following cardiovascular diseases [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. After a median follow-up for 3 years, 10.4% sepsis survivors experienced a major cardiovascular event, of which hypertension and atrial fibrillation were associated with 34% and 46% enhanced risks for major cardiovascular events [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e]. Consistent with previous investigations, we also observed that hypertension, ischemic heart disease and atrial fibrillation act as important mediators driving many subsequent diseases in sepsis patients, for example, the trajectories from atrial fibrillation to subsequent and acute renal failure, as well as the trajectory from ischemic heart disease to anemia. Importantly, hypertensive disorder not only drive many downstream disease trajectories but also link circulatory, respiratory, and neoplastic death in sepsis survivors. This indicates that blood pressure control is crucial for sepsis survivors to prevent a subsequent series of cardiometabolic adverse events.\u003c/p\u003e \u003cp\u003eConsistent with previous findings [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e], another major intermediary disease leading to a series of downstream trajectories is diabetes. The association between sepsis and subsequent diabetes remains significant even when excluding individuals with a history of any disease belonging to the metabolic system. Although some debates still exist in the association between diabetes and sepsis [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], previous study reported that infectious diseases increased risk of subsequent diabetes [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e]. Evidence also supported that diabetes directly (association with other comorbidities) [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] or indirectly (release of metabolic biomarkers or inflammatory factors induced by infection)[\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e] influence subsequent cardiovascular diseases. One previous study reported that adult sepsis survivors with pre-existing diabetes increased risks of subsequent myocardial infarction and stroke [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Correspondingly, we also identified a trajectory originating from diabetes to hypertensive disorders, which subsequently affected a range of downstream conditions in the circulatory, musculoskeletal, and digestive systems. The possible biological mechanisms linking sepsis to cardiometabolic may attribute to release of inflammatory and metabolic factors, triggering a latent pro-thrombotic state or damaging the vascular endothelium after the infection [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e, \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e]. In addition, in vivo experiments have revealed the potential molecular mechanisms involved [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e], and their genetic correlation has also been identified between sepsis and cardiometabolic diseases [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAs expected, we found sepsis associated with increased risk of following acute kidney failure, which have been widely confirmed in previous findings [\u003cspan additionalcitationids=\"CR48\" citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Herein, the mediated role of atrial fibrillation and asthma in sepsis survivors, linking the progression to acute renal failure with subsequent CKD, were also observed. Consistent with previous study reported that 61% sepsis patients progressed to AKI then 19% patients further developed to CKD after 1 year follow up, circulatory system diseases are risk factors involved in the development from sepsis to CKD [\u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. Sepsis with following AKI is a complex clinical disorder, the possible mechanisms included inflammation and endothelial dysfunction, aberrant in blood flow and vascular permeability, coagulation imbalance, and renal inflammation attributed to hypertension and diabetes in septic patients [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e, \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWe also observed sepsis impact subsequent incidence of respiratory and infectious system diseases, although few studies have focused on these associations. One previous report showed that higher systemic immune-inflammation index level was independently associated with an elevated likelihood of COPD [\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]. In line with our study, COPD was reported to be an independent risk factor for mortality in patients with sepsis [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e]. Although one previous Mendelian randomization reported nonsignificant causality from sepsis to asthma [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e], the associations between sepsis and respiratory system diseases still need to be further explored. Previous studies have demonstrated that sepsis increases the risk of gastrointestinal bleeding [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e, \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e], which might be one of the susceptibility factors for subsequent gastrointestinal infection [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. It is inferred that immune system change and inflammatory factor release and microbiome alterations in lung [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e] and gastrointestinal [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e] caused by sepsis [\u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e62\u003c/span\u003e], as well as shared inflammatory pathways between them [\u003cspan additionalcitationids=\"CR64\" citationid=\"CR63\" class=\"CitationRef\"\u003e63\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e65\u003c/span\u003e] may be the possible biological mechanism linking sepsis and these diseases.\u003c/p\u003e \u003cp\u003eIn subgroup analyses, trajectories leading to death are also much complex in male than in female, in line with previous study showing that males with sepsis may experience a less favorable outcome than females [\u003cspan citationid=\"CR66\" class=\"CitationRef\"\u003e66\u003c/span\u003e]. Many studies have reported that female had lower risk for following cardiometabolic diseases [\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e], respiratory system diseases [\u003cspan citationid=\"CR68\" class=\"CitationRef\"\u003e68\u003c/span\u003e] as compared to male. The underlying mechanisms for these differences may result from hormone [\u003cspan citationid=\"CR69\" class=\"CitationRef\"\u003e69\u003c/span\u003e] and unhealthy lifestyle in male [\u003cspan citationid=\"CR70\" class=\"CitationRef\"\u003e70\u003c/span\u003e]. An additional interesting finding from the subgroup analysis is that we observed much more complex disease trajectories, as well as trajectories leading to death, among younger sepsis patients compared to older individuals. Consistently, several previous research found more pronounced risk for cardiovascular events in younger sepsis patients [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. These results indicated that sepsis might be an independent risk factor for subsequent many disease categories, as younger individuals were less influenced by baseline comorbidities or other risk factors, thus highlighting the importance of interventions in sepsis patients to prevent subsequent cardiovascular diseases [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe major strength of our study was the use of a largely population-based prospective cohort. We depicted the network of disease trajectories after sepsis and identified both commonly known and novel unknown trajectories. This visualized disease network enables us to holistically explore the development of diseases after sepsis, rather than focusing solely on the association between two separate diseases. For sepsis patients, prevention or intervention for mediators such as hypertension, diabetes, and atrial fibrillation is highly important, as they may drive many downstream health conditions. These findings may provide potential clues for further investigating the biological linkages within this trajectory network and promoting the overall health of sepsis survivors.\u003c/p\u003e \u003cp\u003eThere are also some limitations in our study. Firstly, sepsis is a heterogeneous disease; the signs and symptoms of sepsis are nonspecific, and there is no \u0026ldquo;gold standard\u0026rdquo; test for sepsis diagnosis [\u003cspan citationid=\"CR71\" class=\"CitationRef\"\u003e71\u003c/span\u003e]. Although many studies have used 3-digit ICD codes for sepsis identification, the insufficient accuracy in sepsis definition may have affected the research conclusions. Therefore, to illustrate the impact of sepsis heterogeneity on the outcome, a series of sensitivity analyses were performed. We found that the results were not largely changed after maintaining sepsis cases defined by primary ICD codes, excluding severe sepsis cases or excluding individuals with the same disease history as the medical condition. In addition, sepsis may be underestimated in UK Biobank due to the absence of explicit ICD-10 diagnosis codes despite documented coexistence of infection and organ dysfunction. Secondly, sepsis is usually caused by other diseases prior to sepsis, these preexisting diseases might influence trajectory after sepsis. To eliminate the impact of other diseases, we adjusted CCI at baseline considering the influence of comorbidities before sepsis, and the results largely remained significant. In addition, we further restricted septic patients who do not had any other diagnosis within 6 months or1 year prior to sepsis in sensitivity analysis and found most results still unchanged. Thirdly, some rare diseases with low incidence might have been neglected here due to the relatively limited follow-up period in the current study (median 3.99 years), which may influence observed results. Additional research with a longer follow-up is needed to explore subsequent diseases with later onset after sepsis. Fourthly, to further confirm the reliability of the results in our study, external validation of disease trajectories using other large-scale population-based cohorts is further needed. Fifthly, although some trajectories identified in our study were validated using sequential pattern mining algorithm or other online tools, the causal inference among these disease pairs has not been fully demonstrated. In addition, we mainly focused on the disease trajectories originating from sepsis; some disease pairs might have a bidirectional relationship that was not fully explored in our study. Sixthly, using the community-based UK Biobank, the matched sepsis-free controls might be much healthier than sepsis patients. To mitigate possible bias, we conducted sensitivity analysis by individually matching sepsis cases with up to 10 hospitalized sepsis-free controls from the UK Biobank, and found that sepsis was no longer significantly associated with subsequent diabetes, hypertensive disorders, or asthma. This may be attributable to the high prevalence of these common conditions among hospitalized patients. As more data become available, further in-depth investigation into the associations between sepsis and these diseases is needed. Lastly, we provide possible evidence for subsequent disease trajectories after the sepsis diagnosis, but the physiological or biological mechanisms among these disease pairs need to be further explored in the future.\u003c/p\u003e \u003cp\u003eIn conclusion, this community-based study showed a visualized network of disease trajectory, and demonstrated the increased risk for following cardiometabolic, respiratory, metabolic, and genitourinary system diseases or mortality after sepsis diagnosis. We identified hypertension, diabetes and atrial fibrillation act as an important mediator driving many following diseases or mortalities in sepsis patients, for example the following trajectories to ischemic heart disease and heat failure, as well as acute renal failure and CKD from these intermediated diseases in sepsis survivors. Furthermore, we also conducted comprehensive subgroup analyses for disease trajectories and leading causes of death in male and female, as well as young and old sepsis patients. Exploring potential interventions targeting these trajectories is of great importance, as it would prevent health declines and promote the quality of life for sepsis survivors.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eWHO: World Health Organization\u003c/p\u003e\n\u003cp\u003eAKI: acute kidney injury\u003c/p\u003e\n\u003cp\u003eCOPD: chronic obstructive pulmonary disease\u003c/p\u003e\n\u003cp\u003eSMR: Scottish Morbidity Record\u003c/p\u003e\n\u003cp\u003eICD: International Classification of Disease\u003c/p\u003e\n\u003cp\u003eCOVID-19: Coronavirus disease 2019\u003c/p\u003e\n\u003cp\u003ePheWAS: Phenome-wide association analysis\u003c/p\u003e\n\u003cp\u003eHR: hazard ratio\u003c/p\u003e\n\u003cp\u003eOR: odds ratio\u003c/p\u003e\n\u003cp\u003eCCI: Charlson comorbidity index\u003c/p\u003e\n\u003cp\u003eIQR: interquartile range\u003c/p\u003e\n\u003cp\u003eCKD: chronic kidney disease\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor information\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors and Affiliations\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDivision of Nephrology and West China Biomedical Big Data Center, West China Hospital, Sichuan University, Chengdu, 610041, China\u003c/p\u003e\n\u003cp\u003eMed-X Center for Informatics, Sichuan University, Chengdu, 610065, China\u003c/p\u003e\n\u003cp\u003eChunyang Li, Chao Zhang, Zhiye Ying, Xiaoxi Zeng\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDepartment of Critical Care Medicine, West China Hospital, Sichuan University, Chengdu, Sichuan 610041, China.\u003c/p\u003e\n\u003cp\u003eBo Wang\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDepartment of Core Laboratory, Sichuan Provincial People\u0026apos;s Hospital, University of Electronic Science and Technology of China, Chengdu, China\u003c/p\u003e\n\u003cp\u003eJie Chen, Wenyi Zhang\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCYL and XXZ conceived of the idea, CYL and CZ performed analyses and wrote the manuscript, CZ prepared tables and figures, BW, JC and WYZ interpreted the results, ZYY prepared software and computing platform, XXZ supervised the study and revised the whole manuscript. \u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCorresponding author\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eXiaoxi Zeng ([email protected]), Division of Nephrology, West China Biomedical Big Data Center, West China Hospital, Sichuan University, 37 Guo Xue Xiang, Chengdu, Sichuan, 610041, China\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the UK Biobank participants provided written informed consent before data collection. The UK Biobank has full ethical approval from the National Health Service (NHS) National Research Ethics Service (16/NW/0274). This study was approved by the Biomedical Research Ethics Committee of West China Hospital (2019\u0026ndash;1171).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article can be applied from the UK Biobank (http://www.ukbiobank.ac.uk/register-apply).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eKey Research and Development Program of the Ministry of Science and Technology of the People\u0026apos;s Republic of China (2022YFC2504501).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was conducted using the UK Biobank Resource under Application 54803. This work uses data provided by patients and collected by the National Health Service as part of their care and support. This research used data assets made available by National Safe Haven as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref: MC_PC_20029 and MC_PC_20058).\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eM Singer CSD, Seymour CW. The third international consensus definitions for sepsis and septic shock (Sepsis-3). JAMA. 2016.\u003c/li\u003e\n\u003cli\u003eRudd KE, Johnson SC, Agesa KM, Shackelford KA, Tsoi D, Kievlan DR, et al. Global, regional, and national sepsis incidence and mortality, 1990-2017: analysis for the Global Burden of Disease Study. Lancet. 2020;395(10219):200-11.\u003c/li\u003e\n\u003cli\u003eLiu V, Escobar GJ, Greene JD, Soule J, Whippy A, Angus DC, et al. 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J Transl Med. 2023;21(1):902.\u003c/li\u003e\n\u003cli\u003eWang Y, Liu W, Xu Y, He X, Yuan Q, Luo P, et al. Revealing the signaling of complement receptors C3aR and C5aR1 by anaphylatoxins. Nat Chem Biol. 2023;19(11):1351-60.\u003c/li\u003e\n\u003cli\u003eZhao T, Su Z, Li Y, Zhang X, You Q. Chitinase-3 like-protein-1 function and its role in diseases. Signal Transduct Target Ther. 2020;5(1):201.\u003c/li\u003e\n\u003cli\u003eShields CA, Wang X, Cornelius DC. Sex differences in cardiovascular response to sepsis. Am J Physiol Cell Physiol. 2023;324(2):C458-458C466.\u003c/li\u003e\n\u003cli\u003eWolter NL, Jaffe IZ. Emerging vascular cell-specific roles for mineralocorticoid receptor: implications for understanding sex differences in cardiovascular disease. Am J Physiol Cell Physiol. 2023;324(1):C193-193C204.\u003c/li\u003e\n\u003cli\u003eGBD 2019 LRI Collaborators, . Age-sex differences in the global burden of lower respiratory infections and risk factors, 1990-2019: results from the Global Burden of Disease Study 2019. Lancet Infect Dis. 2022;22(11):1626-47.\u003c/li\u003e\n\u003cli\u003eReckelhoff JF. Androgens and Blood Pressure Control: Sex Differences and Mechanisms. Mayo Clin Proc. 2019;94(3):536-43.\u003c/li\u003e\n\u003cli\u003eBouwman E, Penson A, de Valk M, van den Oever SR, van der Pal H, van Dulmen-den Broeder E, et al. Unhealthy lifestyle behaviors, overweight, and obesity among childhood cancer survivors in the Netherlands: A DCCSS LATER study. Cancer. 2024;130(16):2856-72.\u003c/li\u003e\n\u003cli\u003eEvans L, Rhodes A, Alhazzani W, Antonelli M, Coopersmith CM, French C, et al. Surviving sepsis campaign: international guidelines for management of sepsis and septic shock 2021. Intensive Care Med. 2021;47(11):1181-247.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Sepsis, disease trajectory, mortality, cause of death, disease network","lastPublishedDoi":"10.21203/rs.3.rs-5886414/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5886414/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eSepsis is a life-threatening disease and among the most common cause of death, which influence a series of following medical conditions. A comprehensive analysis of the cross-talks with temporary order of disease trajectory or trajectory led to cause of death following sepsis remains unclear. By using data from population-based UK Biobank, 8647 septic patients matching with 86386 controls without sepsis were included. Individuals were followed-up from 1 month after the index date until the end of 2019 with linkage of inpatient or death records to the registers. Then conditional Cox regression, binomial test together with conditional logistic regression were conducted to visualize the disease trajectories and trajectories leading to cause of death in sepsis survivors. During a median follow-up of 3.99 years, sepsis significantly increased the risk of 113 subsequent medical conditions. By visualizing disease-disease associations with time-dependent sequence, we identified four main affected disease clusters after sepsis, including circulatory, metabolic, respiratory and genitourinary system disease, further linking a series of downstream health outcomes. We also identified trajectories leading to mortality in three major categories of death in sepsis survivors, which were neoplastic, circulatory and respiratory system disease. In addition, disease trajectory after sepsis differed in gender and age groups were also explored in our study. These trajectory networks visualize a series of pathways linking sepsis to a broad range health conditions and provide potential intervention targeting these diseases for inhibiting adverse events in sepsis patients.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e","manuscriptTitle":"Disease trajectory and mortality among sepsis patients: a prospective cohort study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-10 06:35:55","doi":"10.21203/rs.3.rs-5886414/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-09-15T13:43:38+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-09-15T13:43:08+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-28T16:36:04+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"196954638358917928802389952452082856282","date":"2025-04-08T04:45:09+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-05T20:54:49+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-03-31T05:02:22+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Infectious Diseases","date":"2025-03-28T01:21:35+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-infectious-diseases","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"infd","sideBox":"Learn more about [BMC Infectious Diseases](http://bmcinfectdis.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/infd","title":"BMC Infectious Diseases","twitterHandle":"#bmcinfectdis","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"afe30a54-637f-43e0-8123-3f542dce24da","owner":[],"postedDate":"April 10th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-10-20T16:05:23+00:00","versionOfRecord":{"articleIdentity":"rs-5886414","link":"https://doi.org/10.1186/s12879-025-11701-z","journal":{"identity":"bmc-infectious-diseases","isVorOnly":false,"title":"BMC Infectious Diseases"},"publishedOn":"2025-10-14 15:58:39","publishedOnDateReadable":"October 14th, 2025"},"versionCreatedAt":"2025-04-10 06:35:55","video":"","vorDoi":"10.1186/s12879-025-11701-z","vorDoiUrl":"https://doi.org/10.1186/s12879-025-11701-z","workflowStages":[]},"version":"v1","identity":"rs-5886414","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5886414","identity":"rs-5886414","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}