Clinical phenotypic characteristics of heart failure with preserved ejection fraction and its influence on prognosis

Clinical phenotypic characteristics of heart failure with preserved ejection fraction and its influence on prognosis | 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 Clinical phenotypic characteristics of heart failure with preserved ejection fraction and its influence on prognosis Xia Xu, Yajiao Wang, Yumeng Li, Bingxuan Zhang, qingqiao song This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3278169/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Patients with heart failure with preserved ejection fraction are characterized by high morbidity and poor prognosis. Previous studies have shown that there are several different phenotypes of HFpEF, each with distinct clinical features, and we used k-means clustering to determine the clinical phenotypes of patients with HFpEF and to investigate their impact on prognosis. Methods We first screened 189 patients with HFpEF who met the inclusion criteria and stratified them using K-mean clustering according to clinical characteristics, routine blood and biochemical parameters, echocardiography, and comorbidities, and determined the optimal number of prime hearts using the error sum of squares. Kaplan-Meier survival curves were then used to assess the impact of each clinical phenotype on all-cause mortality; Cox regression risk models were used to estimate the correlation between each clinical phenotype and long-term prognosis. Results Four HFpEF phenotypes were identified: phenotype 1 was a young patient with poor cardiac function but preserved renal function; phenotype 2 was an older male patient with cardiac and renal insufficiency; phenotype 3 had preserved LA morphology and function, and all patients in this group had higher ejection fractions than the other three groups; phenotype 4 was an older female patient with preserved cardiac function but poor renal function. The Kaplan-Meier survival analysis found that patients with phenotype 2 had significantly lower survival rates than the other three groups, and the Cox proportional risk analysis also found that phenotype 2 showed the highest risk of all-cause mortality (HR = 4.6094; 95% CI: 2.0373, 10.4291). Conclusion K-means cluster analysis classified HFpEF patients into four clinical phenotypes, and the analysis revealed that old age and renal insufficiency were decisive factors affecting prognosis, so the staging and treatment of HFpEF patients should focus on age and renal function. heart failure with preserved ejection fraction machine learning K-means clustering renal insufficiency cardiorenal syndrome Figures Figure 1 Figure 2 Figure 3 Introduction Heart failure (HF) is the terminal stage of various heart diseases( 1 , 2 ), which is characterized by high morbidity, high hospitalization rate and high mortality( 3 , 4 ). The 2016 European Society of Cardiology guidelines divide HF into three categories, which are mainly stratified based on ejection fraction: HF with reduced ejection fraction (HFrEF), with EF less than 40%; HF with medium ejection fraction (HFmrEF), EF is between 40% and 50%; HF with preserved ejection fraction (HFpEF), where EF is greater than 50%( 5 ). In patients with signs and symptoms of heart failure, about half of the left ventricular ejection fraction is not significantly abnormal, but the morbidity and mortality of HFpEF patients are almost similar to those of HF and reduced ejection fraction (HFrEF). Therefore, in the past In the past 20 years, people's awareness of the severity of the HFpEF problem has stimulated the explosive growth of clinical research. HFpEF is not a well-defined clinical disease. It is a mixture of cardiovascular, metabolic, renal and geriatric diseases( 6 , 7 ). In addition, patients with HFpEF have highly variable basic cardiac structure and dysfunction. Previous studies have suggested that there are several differences in HFpEF. Phenotype, each phenotype has obvious clinical characteristics, and the HFpEF phenotype group may be related to the obvious difference in the prognosis of the disease. Therefore, identifying the clinical features in each phenotypic group may help to understand the specific mechanism and prognosis of the disease. Materials and Methods Research Object This study is a prospective cohort study. A total of 189 inpatients with HFpEF in the Department of Cardiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences from January 2006 to August 2014 were selected as the research objects. Inclusion criteria: ①Meet the diagnostic criteria of HFpEF, namely HF with preserved ejection fraction (HFpEF), where EF is greater than 50%; ②NT-proBNP༞125ng/L; ③Left ventricular hypertrophy and/or left atrium enlargement, or diastolic function Abnormal; ④Complete baseline data; ⑤Unlimited age and gender; ⑥All patients voluntarily signed an informed consent form. Exclusion criteria: ①Patients with severe heart damage, such as severe valvular disease, acute coronary (coronary artery) syndrome, malignant arrhythmia, etc.; ②With severe systemic diseases, such as liver cirrhosis, end-stage renal disease, and malignant tumors Etc.; ③Cognitive impairment, severe mental illness, or other diseases cause the patient to lack self-control ability and unable to cooperate; ④The follow-up period is suddenly interrupted, and the follow-up period does not reach 730 days. Baseline Data Collection After the patient is hospitalized, collect the patient’s name, gender, age, hospitalization date, disease course, cardiac function classification (NYHA), complications and other basic information, and collect blood routines, biochemistry, CRP, BNP and other blood indicators to improve the dynamic electrocardiogram, Cardiac ultrasound and other examinations. Patient Follow-up After the patients are discharged from the hospital, specially trained investigators will conduct outpatient, inpatient, or telephone follow-up of qualified patients. The follow-up period is 730 days, with all-cause death as the main follow-up endpoint. During the hospitalization period and the follow-up period, the research subjects did not interfere with their treatment measures. K-means Clustering We use the python package to implement k-means clustering to stratify patients with HFpEF. Cluster analysis is the most commonly used category of unsupervised machine learning algorithms. Its purpose is to divide data into meaningful or useful groups. K-means clustering is also called K-means clustering. It tries to find K different clusters, and the center of each cluster is calculated by using the mean value of the values contained in the cluster. The steps of the K-means clustering algorithm are as follows: First, import the data set, calculate the optimal K parameters according to the algorithm, and determine K initial centroids; then assign each point in the data set to the nearest centroid, and assign it to a centroid. The points are a cluster. Then, according to the points assigned to the cluster, the centroid of each cluster is updated to the average value of all the points of the cluster, and the assignment and update steps are repeated until the cluster does not change, or equivalently, until the centroid does not change. As we all know, the specified number of clusters has a great impact on the performance of K-means clustering, so this study uses the sum of squares of errors (SSE) to determine the best K value. SSE is the most important evaluation index of the clustering algorithm model. Its main function is to combine the distance calculation method to derive the centroid selection method. For a certain data set, the horizontal axis can be drawn as the number of centroids, and the vertical axis can be a curve with the sum of squares of errors. The curve can be judged. The descending trend of, find a certain inflection point of sudden decline as the reference value of the number of cluster classifications. Statistical Analysis In this study, statistical software such as SPSS 25.0 and MedCalc 19.5.2 was used for statistical analysis. The measurement data conforming to the normal distribution were expressed as x ± s, and the independent sample t test was used for comparison between groups. The measurement data that does not conform to the normal distribution are represented by the median (upper quartile, lower quartile) [M(Q1, Q3)], Mann-Whitney test is used for comparison between groups, and Kruskal is used for comparison between groups -Wallis H test. For the number of count data use cases (percentage), the chi-square test is used for comparison. Kaplan-Meier survival analysis was used to estimate the all-cause mortality of each type of HFpEF, and the Cox regression risk model was used to observe the correlation between each type and long-term prognosis. The difference was statistically significant with bilateral P < 0.05. Results Patient characteristics, renal function, hemoglobin levels, cardiac function and cardiovascular event rates are shown in Table 1 .In this study, these 30 characteristics were applied to k-means clustering. According to the most important index of error sum of squares using k-means clustering model to determine the model optimization objective, combined with the Euclidean distance calculation method and then draw the learning curve of the number of clustering categories, the number of masses is 1 when the SSE value is too large, so it is recommended to start from 2, as shown in Fig. 1 , at this time it can be seen that the SSE decreases faster when the number of masses increases from 2 to 3 and 4, and from 4 onwards as the number of masses increases Therefore, it is better to select 3 or 4 centers of mass, where the clustering effect is better when 4 centers of mass are selected than 3 centers of mass, so the best k is 4. The coordinates of the clustered centers of mass are shown in Table 2 .HFpEF was stratified by k-means clustering using principal component analysis and divided into 4 groups, and the results are shown in Fig. 2 . Table 1 Patients’ characteristics. Patients with HFpEF (n = 189) Male 77(40.74) Age,years 78(74, 82) Course of disease ,years 5(2, 13.5) LVEDD,mm 46(42, 50) RV,mm 23.5(21, 26) LVPWT,mm 9(9, 10) IVST,mm 10(9, 11) LA,mm 39(35, 44) RA,mm 36(34, 42) LVEF,% 55(52, 58) FS,mm 0.29(0.27, 0.31) MVR 140(74.07) MRTR 126(66.66) PR 17(8.99) AR 99(52.38) WBC,10 9 /L 6.375(5.26, 7.7425) Hb,g/L 122.5(102.75, 135.25) CRP,mg/L 7.03(2.79,20) Scr,mg/dL 85(68.4, 114) BUN,mmol/L 6.8(5.07, 9) eGFR, ml/min/1.73 m2 67.76(45.19, 79.5) TC,mmol/L 3.87(3.23, 4.59) TG,mmol/L 1.13(0.82,1.54) LDL-C,mmol/L 2.16(1.82, 2.82) NT-proBNP,ng/L 2212(1384, 3788) Coronary Heart Disease 158(83.59) Diabetes mellitus 60(31.74) Hypertension 147(77.77) Hyperlipidemia 102(53.96) Table 2 Coordinates of cluster centroids. Group 1 Group 2 Group 3 Group 4 Male -0.056 0.027 0.085 -0.080 Age,years 0.004 -0.035 0.093 -0.077 Course of disease ,years 0.078 0.007 0.012 -0.049 LVEDD,mm 3.167 -1.341 0.209 -0.743 RV,mm 1.263 1.642 -1.306 -0.161 LVPWT,mm -0.007 -0.600 0.271 0.082 IVST,mm 0.806 0.127 -0.495 0.098 LA,mm -0.034 -0.277 -0.618 0.817 RA,mm 0.240 0.237 0.272 -0.523 LVEF,% -0.064 -0.452 -0.143 0.444 FS,mm -0.174 0.635 0.151 -0.462 MVR -0.017 -0.045 0.112 -0.082 MRTR -0.094 0.374 0.100 -0.287 PR -0.449 -0.022 0.235 -0.042 AR -0.126 0.204 0.058 -0.129 WBC,10 9 /L 0.215 0.155 -0.163 -0.015 Hb,g/L 0.031 -0.147 0.056 0.017 CRP,mg/L -0.272 0.063 -0.063 0.141 Scr,mg/dL 0.032 0.028 0.093 -0.126 BUN,mmol/L -0.204 0.061 0.135 -0.090 eGFR, ml/min/1.73 m2 -0.056 -0.084 0.141 -0.072 TC,mmol/L 0.185 -0.159 -0.034 0.052 TG,mmol/L 0.024 -0.115 -0.045 0.106 LDL-C,mmol/L -0.124 -0.119 0.131 -0.012 NT-proBNP,ng/L 0.017 -0.221 0.079 0.043 Coronary Heart Disease 0.047 -0.058 0.011 0.004 Diabetes mellitus -0.071 0.054 0.009 -0.012 Hypertension -0.117 0.042 -0.018 0.042 Hyperlipidemia -0.013 0.047 -0.055 0.034 NYHA 0.043 -0.006 0.002 -0.017 Stratification of HFpEF Using K-MeansClustering Basic Characteristics of Patients with HFpEF HFpEF patient characteristics are shown in Table 3 . Group 1 consisted of younger individuals (median age 76 years) with relatively preserved renal function (median eGFR 75.24 ml/min/1.73 m2), and relatively more normal lipids. Group 2 was characterized by older age (median age 80 years), the highest proportion of males (54.84%), higher Scr, BUN and lower eGFR (median 33.98 ml/min/1.73 m2), and higher markers of inflammatory response such as WBC and CRP. Group 3 exhibited intermediate age (median age 78 years) and a relatively short disease duration. Group 4 was characterized by older age (median age 82 years), the highest proportion of women (91.3%), higher lipid levels (median TC 5.26 mmol/l, median TG 2.1 mmol/l and median LDL-C 3.55 mmol/l) and lower eGFR (median 46.08 ml/min/1.73 m2). Table 3 Patient characteristics and Blood routine, biochemical indicators according to stratification using k-means clustering. Group 1 (n = 44) Group 2 (n = 31) Group 3 (n = 91) Group 4 (n = 23) H/ P Age,years 76(67.5, 79.5) 80(75,84.75) ✱ 78(74,82) # 82(77,84.75) ★♢ 14.3877 0.002422 Male 17(38.64) 17(54.84) 41(45.05) 2(8.7) ♢ 13.117 0.0044 Course of disease ,years 7.5(2,16.5) 7(2,18) 4.2(1,10) 10(5,20) ♢ 7.9003 0.048118 WBC,10 9 /L 5.57(4.51,6.57) 7.11(5.42,8.328) ✱ 6.51(5.222,7.605) # 7.85(6.275,11.303) ★♢ 22.6296 0.000048 Hb,g/L 124.5(111.5,136.5) 102(91.75,128.25) ✱ 127(107,138) ◑ 113(01.25,130.25) 9.3175 0.025354 CRP,mg/L 4.255(1.6,8.515) 10.76(3.838,41.18) ✱ 9(2.523,26.77) # 5.68(3, 7.965) 9.6650 0.021640 Scr,mg/dL 76(62,85) 147(117.75,171) ✱ 76(64.075,95.95) ◑ 105.5(85.5,128.6) ★▲♢ 69.9252 <0.000001 BUN,mmol/L 7.105(5.015,8.8) 10.5(8.163,14.637) ✱ 5.8(4.677,7.237) #◑ 7.81(5.72,9.73) ▲♢ 49.9622 <0.000001 eGFR, ml/min/1.73 m2 75.24(62.975,85.23) 33.98(27.83,45.86) ✱ 72.15(63.437,83.602) ◑ 46.08(34.99,57.87) ★▲♢ 79.9090 <0.000001 TC,mmol/L 3.295(2.725,3.59) 3.89(3.233,4.607) ✱ 3.89(3.253,4.378) # 5.26(4.597,6.185) ★▲♢ 45.0152 <0.000001 TG,mmol/L 0.83(0.725,1.115) 1.15(0.965,1.46) ✱ 1.16(0.85,1.465) # 2.1(1.372,2.803) ★▲♢ 36.8201 <0.000001 LDL-C,mmol/L 1.895(1.515,2.105) 2.11(1.845,2.865) ✱ 2.17(1.842,2.598) #◑ 3.55(2.827,4.155) ★▲♢ 50.4748 <0.000001 Number of patients (%), median (interquartile range). ✱, the comparison between the 1st group and the 2nd group, p < 0.05; #, the comparison between the 1st group and the 3rd group, p < 0.05; ★, the comparison between the 1st group and the 4th group, p < 0.05; ◑, comparison between group 2 and group 3, p < 0.05; ▲, comparison between group 2 and group 4, p < 0.05; ♢, comparison between group 3 and group 4 Comparison between the two, p < 0.05. Cardiac Function and Complications in Patients with HFpEF The cardiac function and the occurrence of complications in HFpEF patients are shown in Table 4 . The cardiac function and structure of the first group of patients have changed significantly. The thickness of the left atrium, right atrium, right ventricle, and ventricular septum were larger in all groups, and their median values were LA 46.5mm, RA 52.5mm, RV 27mm, IVST 11mm, and the second, third, and fourth groups were all smaller than the first Group. The second group showed moderate left atrium, right atrium, and right ventricle sizes. Compared with the first group, the function and morphology of LV, RA, and RV were preserved. This group showed a higher NT-ProBNP value (median value 4965pg/mL, P༜0.05), and the proportion of grade IV in the cardiac function classification was the highest (45.16%). Compared with the first and second groups, the shape and function of LA in the third group were preserved. The ejection fraction of the patients in this group was also higher than that of the other three groups, and the incidence of MVR, MRTR, PR, and AR was the lowest. Group 4 showed the lowest cardiac function and structural damage, with lower LV, RA, and RV, and lower levels of NT-ProBNP (median value 1725pg/mL). Table 4 Patient symptoms and sings of HF, cardiac function, and cardiac events according to stratification using k-means clustering. Group 1 (n = 44) Group 2 (n = 31) Group 3 (n = 91) Group 4 (n = 23) H/ P LVEDD,mm 47.5(42,53.5) 46(43.25,51) 46(42,49) 45(43,46.75) 4.4779 0.214273 LVPWT,mm 10(9,10) 9(9,10) 9(9,10) # 9(8,10) ★ 10.7002 0.013463 FS,mm 0.28(0.27,0.33) 0.28(0.27,0.3) 0.3(0.27,0.34) ◑ 0.28(0.27,0.32) 5.4372 0.142445 IVST,mm 11(10,11.85) 10(9,10.75) ✱ 10(9,11) # 10(9,11) 8.2691 0.040766 LA,mm 46.5(40.5,54) 39(36.25,41.75) ✱ 36(33,40) #◑ 37(35,42) ★ 45.6289 < 0.000001 RV,mm 27(25,31.5) 23(21.25,26) ✱ 23(21,24.75) # 22(20,25) ★ 43.3516 < 0.000001 RA,mm 52.5(42,61.5) 36(34,39) ✱ 35(32,37) # 34(31.5,36.75) ★ 77.2389 < 0.000001 LVEF,% 53(52,57) 55(52,58) 56(53,59) # 55(52.3,57.7) 7.7138 0.052311 MVR 35(79.55) 26(83.87) 61(67.03) 18(78.26) 4.794 0.1875 MRTR 42(95.45) 26(83.87) 46(50.55) 12(52.17) 33.349 < 0.0001 PR 8(18.18) 2(6.45) ✱ 4(4.4) 3(13.04) ▲ 7.594 0.0552 AR 32(72.73) 16(51.61) 36(39.56) 15(65.22) 14.826 0.0020 NT-proBNP,ng/L 2445(1352,3968) 4965(2818.25,9031.75) ✱ 1843(1312.25,2531) #◑ 1725(1143.5,2234.75) ★▲ 41.6164 < 0.000001 Coronary Heart Disease 28(63.64) 30(96.77) 78(85.71) 22(95.65) 19.446 0.0002 Diabetes mellitus 11(25.00) 15(48.39) 25(27.47) ◑ 9(39.13) ♢ 6.232 0.1009 Hypertension 28(63.64) 25(80.65) 76(83.52) 18(78.26) 6.975 0.0727 Hyperlipidemia 20(45.45) 11(35.48) 53(58.24) 18(78.26) 7.858 0.0086 NYHA - - - - 8.021 0.5320 I 1(2.28) 0(0.00) 2(2.20) 1(4.35) II 8(18.18) 2(6.45) 16(17.58) 5(21.74) III 20(45.45) 15(48.39) 50(54.95) 12(52.17) IV 15(34.09) 14(45.16) 23(25.27) 5(21.74) Number of patients (%), median (interquartile range). ✱, the comparison between the 1st group and the 2nd group, p < 0.05; #, the comparison between the 1st group and the 3rd group, p < 0.05; ★, the comparison between the 1st group and the 4th group, p < 0.05; ◑, comparison between group 2 and group 3, p < 0.05; ▲, comparison between group 2 and group 4, p < 0.05; ♢, comparison between group 3 and group 4 Comparison between the two, p < 0.05. Relationship Between Clinical Phenotype and Patient Prognosis Kaplan-Meier analysis of HFpEF stratified by k-means clustering is shown in Fig. 3 .The results showed that patients in group 2 had a significantly lower survival rate compared to the other three groups (P < 0.0001).Cox proportional risk analysis is shown in Table 5 .Compared to group 1, group 2 exhibited the highest risk of all-cause mortality (HR = 4.6094; 95% CI: 2.0373, 10.4291; P = 0.0002); followed by group 4 (HR = 2.1047; 95% CI: 0.8273,5.3541), and group 3 had a similar risk of all-cause mortality to group 1 (group 3 HR = 1.2074; 95% CI: 0.5386,2.7064), with no statistically significant differences in either of the latter two groups compared with group 1 (P > 0.05). The risk of all-cause mortality was significantly lower in group 3 compared with group 2 (P = 0.0001), and the risk of all-cause mortality was lower in group 4 compared with group 2 (HR = 0.4592; 95% CI: 0.1886,1.1178), but the difference was not statistically significant (P = 0.0864). The risk of all-cause death was higher in group 4 compared with group 3 (HR = 1.8554; 95% CI: 0.7561,4.5527), but the difference was not statistically significant (P = 0.1771). model 2 corrected for sex, age, and disease duration on the basis of model 1; model 3: corrected for cardiac function classification, NT- proBNP, LVEF and other cardiac function evaluation indexes on the basis of Model 2, and these results were almost the same in the adjusted model (Table 5 ). In summary, it can be seen that among the 4 groups, the risk of all-cause mortality was highest in group 2, followed by group 4, and the risk of all-cause mortality was similarly lower in both groups 1 and 3. Table 5 Association of phenotype group and poor prognosis in Cox proportional hazard analysis Group 1 Group 2 Group 3 Group 4 HR (95% CI ) P HR (95% CI ) P HR (95% CI ) P HR (95% CI ) P Model1 1 - 4.6094 (2.0373, 10.4291) 0.0002 1.2074(0.5386,2.7064) 0.6473 2.1047 (0.8273,5.3541) 0.1183 Model2 1 - 3.0675 (1.3030,7.2213) 0.0103 0.8780 (0.3804,2.0265) 0.7605 1.1987 (0.4357,3.2982) 0.7256 Model3 1 - 2.5534 (1.0005,6.5167) 0.0499 0.8804 (0.3791,2.0445) 0.7671 1.2762 (0.4603,3.5384) 0.6393 Model1 - - 1 - 0.2443(0.1206,0.4949) 0.0001 0.4592(0.1886,1.1178) 0.0864 Model2 - - 1 - 0.2659 (0.1300,0.5438) 0.0003 0.4488 (0.1676,1.2021) 0.1110 Model3 - - 1 - 0.2618 (0.1134,0.6024) 0.0017 0.4191 (0.1348,1.3029) 0.1329 Model1 - - - - 1 - 1.8554(0.7561,4.5527) 0.1771 Model2 - - - - 1 - 1.8018 (0.6576,4.9375) 0.2523 Model3 - - - - 1 - 1.7521 (0.6392,4.8028) 0.2757 Note: Model1: Use the k-means clustering result as the independent variable and all-cause death as the dependent variable; Model2: Adjust age, gender and disease course on the basis of Model1; Model3: Adjust LVEF and NT-proBNP on the basis of Model2, Heart function classification. Discussion Clustering is a branch of unsupervised learning. It divides samples into groups with similar members. A good clustering algorithm should be efficient, reliable, and able to determine related clusters( 8 , 9 ) .K-means clustering is an important and popular technique in data mining. This method first uses randomly selected points as the initial centroids (centers), and then updates these centroids in the iterative process until certain convergence criteria are met( 10 ). The classification of HFpEF patients is complicated by the multidimensional nature of the disease. This study used cluster analysis to describe the four phenotypes of HFpEF patients. Cluster 1 (young patients with poor cardiac function but preserved renal function) The characteristics of cluster 1 are that the average age is relatively small, the heart function is poor, but the kidney function is preserved. Previous studies have shown that young individuals diagnosed with HFpEF and elderly individuals with HFpEF have similar prevalence of LV filling pressure and LV hypertrophy( 11 ). This study found that group 1 had LV diastolic abnormalities, which is the most common cardiac dysfunction in HFpEF. Impaired renal function is common in patients with HF, and the clinical outcome is worse than that of people without impaired renal function. In this group of patients, renal function is preserved, so the incidence of adverse events and the incidence of complications are relatively low. Cluster 2 (Elderly male patients with cardiorenal insufficiency) Phenotype 2 is characterized by older men with poorer cardiac function, the most obvious decline in renal function, higher inflammatory response markers, and the lowest hemoglobin value. Based on these characteristics, the phenotype is similar to one of the previously reported HFpEF phenotypes, namely, elderly patients with renal insufficiency( 12 ). Most of the progressive decline in renal function observed in HF is thought to be secondary to impaired renal perfusion due to decreased cardiac output( 13 ). Excessive sodium retention caused by renal insufficiency may lead to an increase in LV filling pressure. Chronic high LV filling pressure affects RV diastolic function through ventricular interaction. The LV and RV diastolic function of the second group may be deteriorated through these mechanisms( 14 , 15 ). At the same time, both CKD and HF are in a state of exacerbation of chronic inflammation. CRP is a powerful stimulator of tissue factor production by monocytes (a powerful coagulant). High CRP levels indicate left ventricular dysfunction, cardiac hypertrophy and mortality( 16 , 17 ), and It plays an active and complex role in the pathophysiology of CRS. Anemia can cause tissue ischemia and peripheral vasodilation, leading to chronic renal vein congestion with progressive nephron loss and interstitial fibrosis. Chronic anemia can also lead to left ventricular hypertrophy and myocardial cell death due to ischemia and necrosis( 18 , 19 ). Compared with heart failure patients with reduced ejection fraction (HFrEF), the poorer prognosis of HFpEF patients may be related to old age, hypertension, anemia, etc( 20 ). Patients with this phenotype are older, have the lowest hemoglobin value, and most of them have hypertension. After adjusting for complications, the overall prognosis is still the worst. Cluster 3 (Patients with milder symptoms and shorter illness with preserved heart and kidney function) Generally speaking, LV diastolic capacity decreases with age. Phenotype 3 is younger than phenotype 2 and phenotype 4. Therefore, the morphology and function of LA, RV, and RA are preserved, and the ejection fraction is also higher. The other 3 groups. Age is an independent risk factor for HFpEF. A very small number of HFpEF patients are younger than 65 years old( 21 ), and the incidence of HFpEF gradually increases with age( 20 ). The 5-year risk of all-cause death in HFpEF patients increases significantly with age. Patients under 55 years old account for 5.7%, and after 85 years, the proportion has reached 47.7%.This group of patients was younger and had better cardiac and renal function, so they exhibited a lower all-cause mortality rate. Cluster 4 (Elderly female patients with preserved heart function but poor renal function) Phenotype 4 is characterized by older age, the highest proportion of women, higher blood lipid levels, and impaired kidney function. A study of 178 167 Chinese found that women's TC, TG, and LDL-C levels were initially lower than men, but surpassed men in the 51–55 and 61–65 age groups( 22 ). This may be due to the fact that the blood lipids of men decrease faster with age compared with women( 23 ), so the blood lipid levels of elderly women with this phenotype are higher than those of other groups. Based on the results of cross-sectional studies, it is found that HFpEF is mainly a female disease, and the ratio of females to males in hospitalized HFpEF patients is higher( 24 – 26 ). A follow-up of 8–10 years found that compared with men, women were not associated with an increased risk of HFpEF( 27 – 28 ). However, there is renal insufficiency in the reorganized patients, and renal insufficiency is a predictor of readmission and all-cause death in CHF patients, so the incidence of adverse events in this group of patients is second only to the second group. Advantages and Limitations The strengths of our research including a complete HFpEF cohort, using multiple clinical indicators, including detailed phenotypes of heart and kidney functions, and detailed descriptions of basic conditions and complications, and the application of powerful and mature clustering techniques. Our study also has some limitations. First of all, our study has a relatively small sample size, only echocardiographic data, and no detailed phenotypes of arterial structure/function, which results in limitations of the results. In addition, due to the risk of feature overlap, the clustering method we used cannot guarantee to find the best cluster set. However, the principle of the method is relatively simple and the flexibility is high. Therefore, the performance is satisfactory in many cases, and it is still used today widely used. Conclusions We used K-means cluster analysis to identify 4 subgroups of HFpEF patients based on common clinical characteristics, and these subgroups have significant differences in the impact on the prognosis of the disease. Old age and renal insufficiency are decisive factors affecting the prognosis. Therefore, it is necessary to pay attention to these two factors for heart failure patients with HFpEF. At the same time, it is necessary to improve the subgroup classification method to better identify HFpEF subgroups, expand the variables included, and prospectively verify our observations. Declarations Ethics approval and consent to participate All experimental procedures were approved by the Ethics Committee of Guang'anmen Hospital, Chinese Academy of Traditional Chinese Medicine [Clinical Ethics Approval Number: 2018-074-KY-01]. The study was conducted in accordance with the Declaration of Helsinki and the International Ethical Guidelines for Human Biomedical Research issued by the International Committee for the Organization of Medical Sciences. All patients provided written informed consent prior to admission to the hospital" . Consent for publication Not applicable. Availability of data and materials The data and materials in the current study are available from the corresponding author on reasonable request. Acknowledgements Not applicable. Funding National Natural Science Foundation of China (81904191,82004348), Guang'anmen Hospital of China Academy of Chinese Medical Sciences (2018S420), Capital Health Development Research Special Project (2020-2-4153), Major Public Relations Project of Science and Technology Innovation Project of Chinese Academy of Traditional Chinese Medicine (C12021A01603) References Glasenapp A, Derlin K, Wang Y, Bankstahl M, Meier M, Wollert KC, Bengel FM, Thackeray JT. Multimodality Imaging of Inflammation and Ventricular Remodeling in Pressure-Overload Heart Failure. J Nucl Med. 2020;61(4):590–596. doi: 10.2967/jnumed.119.232488 . Bui AL, Horwich TB, Fonarow GC. Epidemiology and risk profile of heart failure. Nat Rev Cardiol. 2011;8(1):30–41. doi: 10.1038/nrcardio.2010.165 . Lavine KJ, Pinto AR, Epelman S, Kopecky BJ, Clemente-Casares X, Godwin J, Rosenthal N, Kovacic JC. The Macrophage in Cardiac Homeostasis and Disease: JACC Macrophage in CVD Series (Part 4). J Am Coll Cardiol. 2018;72(18):2213–2230. doi: 10. 1016/j.jacc. 2018. 08. 2149. Li L, Zhong S, Cheng B, Qiu H, Hu Z. Cross-Talk between Gut Microbiota and the Heart: A New Target for the Herbal Medicine Treatment of Heart Failure? Evid Based Complement Alternat Med. 2020;2020:9097821. doi: 10.1155/2020/9097821 . Ponikowski P, Voors AA, Anker SD, Bueno H, Cleland JG, Coats AJ, Falk V, González-Juanatey JR, Harjola VP, Jankowska EA, Jessup M, Linde C, Nihoyannopoulos P, Parissis JT, Pieske B, Riley JP, Rosano GM, Ruilope LM, Ruschitzka F, Rutten FH, van der Meer P; Authors/Task Force Members; Document Reviewers. 2016 ESC Guidelines for the diagnosis and treatment of acute and chronic heart failure: The Task Force for the diagnosis and treatment of acute and chronic heart failure of the European Society of Cardiology (ESC). Developed with the special contribution of the Heart Failure Association (HFA) of the ESC. Eur J Heart Fail. 2016;18(8):891–975. doi: 10.1002/ejhf.592 . Borlaug BA. Heart failure with preserved and reduced ejection fraction: different risk profiles for different diseases. Eur Heart J. 2013;34(19):1393–5. doi: 10.1093/eurheartj/eht117 . Cleland JG, Pellicori P, Dierckx R. Clinical trials in patients with heart failure and preserved left ventricular ejection fraction. Heart Fail Clin. 2014;10(3):511 – 23. doi: 10.1016/j.hfc . 2014. 04.011. Xu D., Tian Y. A comprehensive survey of clustering algorithms. Annals of Data Science. 2015;2(2):165–193. doi: 10.1007/s40745-015-0040-1 Pirim H, Ekşioğlu B, Perkins AD. Clustering high throughput biological data with B-MST, a minimum spanning tree based heuristic. Comput Biol Med. 2015;62:94–102. doi: 10.1016/j.compbiomed.2015.03.031 . Pourahmad S, Basirat A, Rahimi A, Doostfatemeh M. Does Determination of Initial Cluster Centroids Improve the Performance of K-Means Clustering Algorithm? Comparison of Three Hybrid Methods by Genetic Algorithm, Minimum Spanning Tree, and Hierarchical Clustering in an Applied Study. Comput Math Methods Med. 2020;2020:7636857.doi: 10.1155/2020/7636857 . Tromp J, MacDonald MR, Tay WT, Teng TK, Hung CL, Narasimhan C, Shimizu W, Ling LH, Ng TP, Yap J, McMurray JJV, Zile MR, Richards AM, Anand IS, Lam CSP. Heart Failure With Preserved Ejection Fraction in the Young. Circulation. 2018;138(24):2763–2773. doi: 10.1161/CIRCULATIONAHA.118.034720 . Harada D, Asanoi H, Noto T, Takagawa J. Different Pathophysiology and Outcomes of Heart Failure With Preserved Ejection Fraction Stratified by K-Means Clustering. Front Cardiovasc Med. 2020;7:607760. doi: 10.3389/fcvm.2020.607760 . Kumar U, Wettersten N, Garimella PS. Cardiorenal Syndrome: Pathophysiology. Cardiol Clin. 2019;37(3):251–265. doi: 10.1016/j.ccl.2019.04.001 Ronco C, Haapio M, House AA, Anavekar N, Bellomo R.Cardiorenal syndrome. J Am Coll Cardiol. (2008) 52:1527–39.doi: 10.1016/j.jacc.2008.07.051 Maughan WL, Sunagawa K, Sagawa K. Ventricular systolicinterdependence: volume elastance model in isolated canine hearts.Am J Physiol. (1987) 253:H138-90. doi: 10.1152/ajpheart.1987.253.6.H1381 Kim B-S, Jeon DS, Shin MJ, et al. Persistent Elevation of C-Reactive Protein May Predict Cardiac Hypertrophy and Dysfunction in Patients Maintained on Hemodialysis. AJN 2005;25(3):189–195. doi: 10.1159/000085585 Yeun JY, Levine RA, Mantadilok V, Kaysen GA. C-reactive protein predicts all-cause and cardiovascular mortality in hemodialysis patients. American Journal of Kidney Diseases 2000;35(3):469–476. doi: 10.1016/S0272-6386(00)70200-9 Brezis M, Rosen S. Hypoxia of the Renal Medulla — Its Implications for Disease. New England Journal of Medicine 1995;332(10):647–655. doi: 10.1056/NEJM199503093321006 Denton KM, Shweta A, Anderson WP. Preglomerular and postglomerular resistance responses to different levels of sympathetic activation by hypoxia. J Am Soc Nephrol. 2002;13(1):27–34. doi: 10.1681/ASN.V13127 Campbell RT, Jhund PS, Castagno D, Hawkins NM, Petrie MC, McMurray JJ. What have we learned about patients with heart failure and preserved ejection fraction from DIG-PEF, CHARM-preserved, and I-PRESERVE? J Am Coll Cardiol. 2012;60(23):2349–56. doi: 10.1016/j.jacc.2012.04.064 Chirinos JA. Discerning the Age-Related Heterogeneity in Heart Failure With Preserved Ejection Fraction. J Am Coll Cardiol. 2019;74(5):613–616. doi: 10.1016/j.jacc.2019.06.008 Feng L, Nian S, Tong Z, et al. Age-related trends in lipid levels: a large-scale cross-sectional study of the general Chinese population. BMJ Open. 2020;10(3):e034226. Published 2020 Mar 18. doi: 10.1136/bmjopen-2019-034226 Wong MWK, Braidy N, Pickford R, et al. Plasma lipidome variation during the second half of the human lifespan is associated with age and sex but minimally with BMI. PLoS One. 2019;14(3):e0214141. Published 2019 Mar 20. doi: 10.1371/journal.pone.0214141 Fonarow GC, Stough WG, Abraham WT, Albert NM, Gheorghiade M, Greenberg BH, O'Connor CM, Sun JL, Yancy CW, Young JB; OPTIMIZE-HF Investigators and Hospitals. Characteristics, treatments, and outcomes of patients with preserved systolic function hospitalized for heart failure: a report from the OPTIMIZE-HF Registry. J Am Coll Cardiol. 2007;50(8):768–77. doi: 10.1016/j.jacc.2007.04.064 . Steinberg BA, Zhao X, Heidenreich PA, Peterson ED, Bhatt DL, Cannon CP, Hernandez AF, Fonarow GC; Get With the Guidelines Scientific Advisory Committee and Investigators. Trends in patients hospitalized with heart failure and preserved left ventricular ejection fraction: prevalence, therapies, and outcomes. Circulation. 2012;126(1):65–75. doi: 10.1161/CIRCULATIONAHA. 111.080770 . Yancy CW, Lopatin M, Stevenson LW, De Marco T, Fonarow GC; ADHERE Scientific Advisory Committee and Investigators. Clinical presentation, management, and in-hospital outcomes of patients admitted with acute decompensated heart failure with preserved systolic function: a report from the Acute Decompensated Heart Failure National Registry (ADHERE) Database. J Am Coll Cardiol. 2006;47(1):76–84. doi: 10.1016/j.jacc.2005.09.022 . Ho JE, Lyass A, Lee DS, Vasan RS, Kannel WB, Larson MG, Levy D. Predictors of new-onset heart failure: differences in preserved versus reduced ejection fraction. Circ Heart Fail. 2013;6(2):279–86. doi: 10.1161/CIRCHEARTFAILURE.112.972828 . Brouwers FP, de Boer RA, van der Harst P, Voors AA, Gansevoort RT, Bakker SJ, Hillege HL, van Veldhuisen DJ, van Gilst WH. Incidence and epidemiology of new onset heart failure with preserved vs. reduced ejection fraction in a community-based cohort: 11-year follow-up of PREVEND. Eur Heart J. 2013;34(19):1424–31. doi: 10.1093/eurheartj/eht066 . Additional Declarations No competing interests reported. Supplementary Files Highlights.docx Cite Share Download PDF Status: Posted Version 1 posted 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. 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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-3278169","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":228583329,"identity":"4888475f-03f5-4778-9608-79e50b000797","order_by":0,"name":"Xia Xu","email":"","orcid":"","institution":"China Academy of Chinese Medical Sciences","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Xia","middleName":"","lastName":"Xu","suffix":""},{"id":228583330,"identity":"b7d97747-0fff-431c-a55b-698f44dbf7dd","order_by":1,"name":"Yajiao Wang","email":"","orcid":"","institution":"China Academy of Chinese Medical 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song","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0klEQVRIiWNgGAWjYHACNgaJCgk5fvbGxgcfiNdyxsZYsudws+EMorUwtqUlbriR3ibNQYx6+fbkbQ8s2A4zNtx82CDNwGAnp9tAQIvBmWflBhI8h5kZZyc2GBcwJBubHSCkRSLHTEJC4jAbs3RiQ/IMhgOJ2whpkZ8B0mJwmIdN8mDDYR5itDDcAGlJSJPgkWBsbCZKC8QvB2yA/klsZpxhQIRfQCH2WPKfRP3+48ef//hQYSdHUAsDQ4IBswTCUoLKIVoYiU0no2AUjIJRMEIBAFGDQzbo/KW/AAAAAElFTkSuQmCC","orcid":"","institution":"Guang’anmen Hospital","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"qingqiao","middleName":"","lastName":"song","suffix":""}],"badges":[],"createdAt":"2023-08-19 15:44:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3278169/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3278169/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":42305999,"identity":"d65a432f-8777-4cd7-bd3e-739bab590230","added_by":"auto","created_at":"2023-08-29 14:03:34","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24769,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eResults of clustering model SSE.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3278169/v1/d01280037223530018c8c155.jpg"},{"id":42307823,"identity":"26101c5c-8f30-46b2-891f-8dfa32aaee51","added_by":"auto","created_at":"2023-08-29 14:11:34","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":27916,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eVisualization of the results of k-means clustering.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3278169/v1/ab30d4217fa4efe6f9416426.jpg"},{"id":42306001,"identity":"b0aacbd2-bdb0-4b66-9016-c3b0e4006f69","added_by":"auto","created_at":"2023-08-29 14:03:34","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":31352,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eKaplan-Meier curve of survival probability of each group based on the results of k-means clustering\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"Figure3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3278169/v1/e21225df9d9ced67eb396b2d.jpg"},{"id":44698818,"identity":"33503eb9-c7ed-4e8f-9b5e-4f7ee0789f80","added_by":"auto","created_at":"2023-10-16 14:52:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":562087,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3278169/v1/ce6f053a-408b-4390-8605-66c4d45a29f4.pdf"},{"id":42306003,"identity":"3ad3e50b-d1b4-4e1d-add8-1500445b61e8","added_by":"auto","created_at":"2023-08-29 14:03:34","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":10553,"visible":true,"origin":"","legend":"","description":"","filename":"Highlights.docx","url":"https://assets-eu.researchsquare.com/files/rs-3278169/v1/40e485256d7d9c41e99c688d.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Clinical phenotypic characteristics of heart failure with preserved ejection fraction and its influence on prognosis","fulltext":[{"header":"Introduction","content":"\u003cp\u003eHeart failure (HF) is the terminal stage of various heart diseases(\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e), which is characterized by high morbidity, high hospitalization rate and high mortality(\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e). The 2016 European Society of Cardiology guidelines divide HF into three categories, which are mainly stratified based on ejection fraction: HF with reduced ejection fraction (HFrEF), with EF less than 40%; HF with medium ejection fraction (HFmrEF), EF is between 40% and 50%; HF with preserved ejection fraction (HFpEF), where EF is greater than 50%(\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e). In patients with signs and symptoms of heart failure, about half of the left ventricular ejection fraction is not significantly abnormal, but the morbidity and mortality of HFpEF patients are almost similar to those of HF and reduced ejection fraction (HFrEF). Therefore, in the past In the past 20 years, people's awareness of the severity of the HFpEF problem has stimulated the explosive growth of clinical research.\u003c/p\u003e \u003cp\u003eHFpEF is not a well-defined clinical disease. It is a mixture of cardiovascular, metabolic, renal and geriatric diseases(\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e). In addition, patients with HFpEF have highly variable basic cardiac structure and dysfunction. Previous studies have suggested that there are several differences in HFpEF. Phenotype, each phenotype has obvious clinical characteristics, and the HFpEF phenotype group may be related to the obvious difference in the prognosis of the disease. Therefore, identifying the clinical features in each phenotypic group may help to understand the specific mechanism and prognosis of the disease.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eResearch Object\u003c/h2\u003e \u003cp\u003eThis study is a prospective cohort study. A total of 189 inpatients with HFpEF in the Department of Cardiology, Guang'anmen Hospital, China Academy of Chinese Medical Sciences from January 2006 to August 2014 were selected as the research objects. Inclusion criteria: ①Meet the diagnostic criteria of HFpEF, namely HF with preserved ejection fraction (HFpEF), where EF is greater than 50%; ②NT-proBNP༞125ng/L; ③Left ventricular hypertrophy and/or left atrium enlargement, or diastolic function Abnormal; ④Complete baseline data; ⑤Unlimited age and gender; ⑥All patients voluntarily signed an informed consent form. Exclusion criteria: ①Patients with severe heart damage, such as severe valvular disease, acute coronary (coronary artery) syndrome, malignant arrhythmia, etc.; ②With severe systemic diseases, such as liver cirrhosis, end-stage renal disease, and malignant tumors Etc.; ③Cognitive impairment, severe mental illness, or other diseases cause the patient to lack self-control ability and unable to cooperate; ④The follow-up period is suddenly interrupted, and the follow-up period does not reach 730 days.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Data Collection\u003c/h2\u003e \u003cp\u003eAfter the patient is hospitalized, collect the patient\u0026rsquo;s name, gender, age, hospitalization date, disease course, cardiac function classification (NYHA), complications and other basic information, and collect blood routines, biochemistry, CRP, BNP and other blood indicators to improve the dynamic electrocardiogram, Cardiac ultrasound and other examinations.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePatient Follow-up\u003c/h2\u003e \u003cp\u003eAfter the patients are discharged from the hospital, specially trained investigators will conduct outpatient, inpatient, or telephone follow-up of qualified patients. The follow-up period is 730 days, with all-cause death as the main follow-up endpoint. During the hospitalization period and the follow-up period, the research subjects did not interfere with their treatment measures.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eK-means Clustering\u003c/h2\u003e \u003cp\u003eWe use the python package to implement k-means clustering to stratify patients with HFpEF. Cluster analysis is the most commonly used category of unsupervised machine learning algorithms. Its purpose is to divide data into meaningful or useful groups. K-means clustering is also called K-means clustering. It tries to find K different clusters, and the center of each cluster is calculated by using the mean value of the values contained in the cluster. The steps of the K-means clustering algorithm are as follows: First, import the data set, calculate the optimal K parameters according to the algorithm, and determine K initial centroids; then assign each point in the data set to the nearest centroid, and assign it to a centroid. The points are a cluster. Then, according to the points assigned to the cluster, the centroid of each cluster is updated to the average value of all the points of the cluster, and the assignment and update steps are repeated until the cluster does not change, or equivalently, until the centroid does not change. As we all know, the specified number of clusters has a great impact on the performance of K-means clustering, so this study uses the sum of squares of errors (SSE) to determine the best K value. SSE is the most important evaluation index of the clustering algorithm model. Its main function is to combine the distance calculation method to derive the centroid selection method. For a certain data set, the horizontal axis can be drawn as the number of centroids, and the vertical axis can be a curve with the sum of squares of errors. The curve can be judged. The descending trend of, find a certain inflection point of sudden decline as the reference value of the number of cluster classifications.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eStatistical Analysis\u003c/h2\u003e \u003cp\u003eIn this study, statistical software such as SPSS 25.0 and MedCalc 19.5.2 was used for statistical analysis. The measurement data conforming to the normal distribution were expressed as x\u0026thinsp;\u0026plusmn;\u0026thinsp;s, and the independent sample t test was used for comparison between groups. The measurement data that does not conform to the normal distribution are represented by the median (upper quartile, lower quartile) [M(Q1, Q3)], Mann-Whitney test is used for comparison between groups, and Kruskal is used for comparison between groups -Wallis H test. For the number of count data use cases (percentage), the chi-square test is used for comparison. Kaplan-Meier survival analysis was used to estimate the all-cause mortality of each type of HFpEF, and the Cox regression risk model was used to observe the correlation between each type and long-term prognosis. The difference was statistically significant with bilateral P\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003ePatient characteristics, renal function, hemoglobin levels, cardiac function and cardiovascular event rates are shown in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.In this study, these 30 characteristics were applied to k-means clustering. According to the most important index of error sum of squares using k-means clustering model to determine the model optimization objective, combined with the Euclidean distance calculation method and then draw the learning curve of the number of clustering categories, the number of masses is 1 when the SSE value is too large, so it is recommended to start from 2, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, at this time it can be seen that the SSE decreases faster when the number of masses increases from 2 to 3 and 4, and from 4 onwards as the number of masses increases Therefore, it is better to select 3 or 4 centers of mass, where the clustering effect is better when 4 centers of mass are selected than 3 centers of mass, so the best k is 4. The coordinates of the clustered centers of mass are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.HFpEF was stratified by k-means clustering using principal component analysis and divided into 4 groups, and the results are shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003ePatients\u0026rsquo; characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"2\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\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\u003ePatients with HFpEF (n\u0026thinsp;=\u0026thinsp;189)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e77(40.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge,years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e78(74, 82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCourse of disease ,years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5(2, 13.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDD,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46(42, 50)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRV,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.5(21, 26)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVPWT,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e9(9, 10)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVST,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(9, 11)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e39(35, 44)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e36(34, 42)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e55(52, 58)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFS,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.29(0.27, 0.31)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e140(74.07)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e126(66.66)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(8.99)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e99(52.38)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC,10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.375(5.26, 7.7425)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb,g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e122.5(102.75, 135.25)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP,mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.03(2.79,20)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr,mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e85(68.4, 114)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e6.8(5.07, 9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, ml/min/1.73 m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e67.76(45.19, 79.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.87(3.23, 4.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.13(0.82,1.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.16(1.82, 2.82)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT-proBNP,ng/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2212(1384, 3788)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary Heart Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e158(83.59)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e60(31.74)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e147(77.77)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e102(53.96)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\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\u003eCoordinates of cluster centroids.\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\u003eGroup 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.085\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.080\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge,years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.077\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCourse of disease ,years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.078\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.049\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDD,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.167\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-1.341\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.743\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRV,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-1.306\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.161\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVPWT,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.600\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.271\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVST,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.806\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.495\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.618\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.817\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.240\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.237\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.444\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFS,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.174\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.635\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.151\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.462\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.082\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.374\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.287\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.449\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.129\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC,10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.155\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.163\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.015\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb,g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.147\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP,mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.272\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr,mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.032\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.028\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.093\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.126\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.204\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.061\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.090\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, ml/min/1.73 m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.072\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.185\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.052\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.045\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.106\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.131\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT-proBNP,ng/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.221\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary Heart Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNYHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStratification of HFpEF Using K-MeansClustering\u003c/h2\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003eBasic Characteristics of Patients with HFpEF\u003c/h2\u003e \u003cp\u003eHFpEF patient characteristics are shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. Group 1 consisted of younger individuals (median age 76 years) with relatively preserved renal function (median eGFR 75.24 ml/min/1.73 m2), and relatively more normal lipids. Group 2 was characterized by older age (median age 80 years), the highest proportion of males (54.84%), higher Scr, BUN and lower eGFR (median 33.98 ml/min/1.73 m2), and higher markers of inflammatory response such as WBC and CRP. Group 3 exhibited intermediate age (median age 78 years) and a relatively short disease duration. Group 4 was characterized by older age (median age 82 years), the highest proportion of women (91.3%), higher lipid levels (median TC 5.26 mmol/l, median TG 2.1 mmol/l and median LDL-C 3.55 mmol/l) and lower eGFR (median 46.08 ml/min/1.73 m2).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient characteristics and Blood routine, biochemical indicators according to stratification using k-means clustering.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eGroup 1 (n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup 2 (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup 3 (n\u0026thinsp;=\u0026thinsp;91)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup 4 (n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH/\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge,years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76(67.5, 79.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e80(75,84.75)\u003csup\u003e✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78(74,82)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e82(77,84.75)\u003csup\u003e★♢\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.3877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.002422\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e17(38.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(54.84)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41(45.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2(8.7)\u003csup\u003e♢\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e13.117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0044\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCourse of disease ,years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.5(2,16.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(2,18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.2(1,10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10(5,20)\u003csup\u003e♢\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.9003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.048118\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWBC,10\u003csup\u003e9\u003c/sup\u003e/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.57(4.51,6.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7.11(5.42,8.328)\u003csup\u003e✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.51(5.222,7.605)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.85(6.275,11.303)\u003csup\u003e★♢\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e22.6296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.000048\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHb,g/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e124.5(111.5,136.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e102(91.75,128.25)\u003csup\u003e✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e127(107,138)\u003csup\u003e◑\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e113(01.25,130.25)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.3175\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.025354\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRP,mg/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.255(1.6,8.515)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.76(3.838,41.18)\u003csup\u003e✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(2.523,26.77)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.68(3, 7.965)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.6650\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.021640\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScr,mg/dL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76(62,85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e147(117.75,171)\u003csup\u003e✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76(64.075,95.95)\u003csup\u003e◑\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e105.5(85.5,128.6)\u003csup\u003e★▲♢\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e69.9252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.000001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBUN,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e7.105(5.015,8.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10.5(8.163,14.637)\u003csup\u003e✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5.8(4.677,7.237)\u003csup\u003e#◑\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e7.81(5.72,9.73)\u003csup\u003e▲♢\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e49.9622\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.000001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eeGFR, ml/min/1.73 m2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e75.24(62.975,85.23)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33.98(27.83,45.86)\u003csup\u003e✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.15(63.437,83.602)\u003csup\u003e◑\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e46.08(34.99,57.87)\u003csup\u003e★▲♢\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e79.9090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.000001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTC,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.295(2.725,3.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.89(3.233,4.607)\u003csup\u003e✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.89(3.253,4.378)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5.26(4.597,6.185)\u003csup\u003e★▲♢\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.0152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.000001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTG,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.83(0.725,1.115)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.15(0.965,1.46)\u003csup\u003e✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.16(0.85,1.465)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.1(1.372,2.803)\u003csup\u003e★▲♢\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e36.8201\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.000001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLDL-C,mmol/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.895(1.515,2.105)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.11(1.845,2.865)\u003csup\u003e✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.17(1.842,2.598)\u003csup\u003e#◑\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.55(2.827,4.155)\u003csup\u003e★▲♢\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e50.4748\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;0.000001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNumber of patients (%), median (interquartile range). ✱, the comparison between the 1st group and the 2nd group, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; #, the comparison between the 1st group and the 3rd group, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ★, the comparison between the 1st group and the 4th group, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ◑, comparison between group 2 and group 3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ▲, comparison between group 2 and group 4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ♢, comparison between group 3 and group 4 Comparison between the two, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCardiac Function and Complications in Patients with HFpEF\u003c/h2\u003e \u003cp\u003eThe cardiac function and the occurrence of complications in HFpEF patients are shown in Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e. The cardiac function and structure of the first group of patients have changed significantly. The thickness of the left atrium, right atrium, right ventricle, and ventricular septum were larger in all groups, and their median values were LA 46.5mm, RA 52.5mm, RV 27mm, IVST 11mm, and the second, third, and fourth groups were all smaller than the first Group. The second group showed moderate left atrium, right atrium, and right ventricle sizes. Compared with the first group, the function and morphology of LV, RA, and RV were preserved. This group showed a higher NT-ProBNP value (median value 4965pg/mL, P༜0.05), and the proportion of grade IV in the cardiac function classification was the highest (45.16%). Compared with the first and second groups, the shape and function of LA in the third group were preserved. The ejection fraction of the patients in this group was also higher than that of the other three groups, and the incidence of MVR, MRTR, PR, and AR was the lowest. Group 4 showed the lowest cardiac function and structural damage, with lower LV, RA, and RV, and lower levels of NT-ProBNP (median value 1725pg/mL).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePatient symptoms and sings of HF, cardiac function, and cardiac events according to stratification using k-means clustering.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" 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=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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\u003eGroup 1 (n\u0026thinsp;=\u0026thinsp;44)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eGroup 2 (n\u0026thinsp;=\u0026thinsp;31)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGroup 3 (n\u0026thinsp;=\u0026thinsp;91)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGroup 4 (n\u0026thinsp;=\u0026thinsp;23)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eH/\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eP\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEDD,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.5(42,53.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(43.25,51)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46(42,49)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e45(43,46.75)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.4779\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.214273\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVPWT,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10(9,10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(9,10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(9,10)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(8,10)\u003csup\u003e★\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e10.7002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.013463\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFS,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.28(0.27,0.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.28(0.27,0.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.3(0.27,0.34)\u003csup\u003e◑\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.28(0.27,0.32)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5.4372\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.142445\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIVST,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(10,11.85)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(9,10.75)\u003csup\u003e✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(9,11)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e10(9,11)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.2691\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.040766\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLA,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.5(40.5,54)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(36.25,41.75)\u003csup\u003e✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(33,40)\u003csup\u003e#◑\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e37(35,42)\u003csup\u003e★\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e45.6289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRV,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e27(25,31.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(21.25,26)\u003csup\u003e✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(21,24.75)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22(20,25)\u003csup\u003e★\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e43.3516\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt; 0.000001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRA,mm\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e52.5(42,61.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(34,39)\u003csup\u003e✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35(32,37)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e34(31.5,36.75)\u003csup\u003e★\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e77.2389\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;0.000001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLVEF,%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e53(52,57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e55(52,58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e56(53,59)\u003csup\u003e#\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55(52.3,57.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.7138\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.052311\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMVR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e35(79.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(83.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61(67.03)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18(78.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.794\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1875\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMRTR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e42(95.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e26(83.87)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e46(50.55)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12(52.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e33.349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt; 0.0001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(18.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(6.45)\u003csup\u003e✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4(4.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3(13.04)\u003csup\u003e▲\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.594\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0552\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e32(72.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(51.61)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(39.56)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e15(65.22)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e14.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0020\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNT-proBNP,ng/L\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2445(1352,3968)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4965(2818.25,9031.75)\u003csup\u003e✱\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1843(1312.25,2531)\u003csup\u003e#◑\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1725(1143.5,2234.75)\u003csup\u003e★▲\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e41.6164\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt; 0.000001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCoronary Heart Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(63.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(96.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78(85.71)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e22(95.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e19.446\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDiabetes mellitus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e11(25.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(48.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e25(27.47)\u003csup\u003e◑\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9(39.13)\u003csup\u003e♢\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.1009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypertension\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e28(63.64)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(80.65)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76(83.52)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18(78.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e6.975\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0727\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(45.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e11(35.48)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53(58.24)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e18(78.26)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7.858\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.0086\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNYHA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e8.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.5320\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1(2.28)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1(4.35)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8(18.18)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(6.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(17.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(21.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIII\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20(45.45)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(48.39)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e50(54.95)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e12(52.17)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIV\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e15(34.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(45.16)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e23(25.27)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e5(21.74)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNumber of patients (%), median (interquartile range). ✱, the comparison between the 1st group and the 2nd group, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; #, the comparison between the 1st group and the 3rd group, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ★, the comparison between the 1st group and the 4th group, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ◑, comparison between group 2 and group 3, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ▲, comparison between group 2 and group 4, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05; ♢, comparison between group 3 and group 4 Comparison between the two, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eRelationship Between Clinical Phenotype and Patient Prognosis\u003c/h2\u003e \u003cp\u003eKaplan-Meier analysis of HFpEF stratified by k-means clustering is shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.The results showed that patients in group 2 had a significantly lower survival rate compared to the other three groups (P\u0026thinsp;\u0026lt;\u0026thinsp;0.0001).Cox proportional risk analysis is shown in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e.Compared to group 1, group 2 exhibited the highest risk of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;4.6094; 95% CI: 2.0373, 10.4291; P\u0026thinsp;=\u0026thinsp;0.0002); followed by group 4 (HR\u0026thinsp;=\u0026thinsp;2.1047; 95% CI: 0.8273,5.3541), and group 3 had a similar risk of all-cause mortality to group 1 (group 3 HR\u0026thinsp;=\u0026thinsp;1.2074; 95% CI: 0.5386,2.7064), with no statistically significant differences in either of the latter two groups compared with group 1 (P\u0026thinsp;\u0026gt;\u0026thinsp;0.05). The risk of all-cause mortality was significantly lower in group 3 compared with group 2 (P\u0026thinsp;=\u0026thinsp;0.0001), and the risk of all-cause mortality was lower in group 4 compared with group 2 (HR\u0026thinsp;=\u0026thinsp;0.4592; 95% CI: 0.1886,1.1178), but the difference was not statistically significant (P\u0026thinsp;=\u0026thinsp;0.0864). The risk of all-cause death was higher in group 4 compared with group 3 (HR\u0026thinsp;=\u0026thinsp;1.8554; 95% CI: 0.7561,4.5527), but the difference was not statistically significant (P\u0026thinsp;=\u0026thinsp;0.1771). model 2 corrected for sex, age, and disease duration on the basis of model 1; model 3: corrected for cardiac function classification, NT- proBNP, LVEF and other cardiac function evaluation indexes on the basis of Model 2, and these results were almost the same in the adjusted model (Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). In summary, it can be seen that among the 4 groups, the risk of all-cause mortality was highest in group 2, followed by group 4, and the risk of all-cause mortality was similarly lower in both groups 1 and 3.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eAssociation of phenotype group and poor prognosis in Cox proportional hazard analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" 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=\"left\" 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=\"left\" 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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eGroup 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eGroup 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eGroup 3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003eGroup 4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e(95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e(95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e(95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u003cem\u003eHR\u003c/em\u003e(95% \u003cem\u003eCI\u003c/em\u003e)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.6094\u003c/p\u003e \u003cp\u003e(2.0373, 10.4291)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1.2074(0.5386,2.7064)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.6473\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e2.1047\u003c/p\u003e \u003cp\u003e(0.8273,5.3541)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1183\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.0675\u003c/p\u003e \u003cp\u003e(1.3030,7.2213)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0103\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8780\u003c/p\u003e \u003cp\u003e(0.3804,2.0265)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7605\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.1987\u003c/p\u003e \u003cp\u003e(0.4357,3.2982)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.7256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.5534\u003c/p\u003e \u003cp\u003e(1.0005,6.5167)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.0499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.8804\u003c/p\u003e \u003cp\u003e(0.3791,2.0445)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.7671\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.2762\u003c/p\u003e \u003cp\u003e(0.4603,3.5384)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.6393\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2443(0.1206,0.4949)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4592(0.1886,1.1178)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.0864\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2659\u003c/p\u003e \u003cp\u003e(0.1300,0.5438)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4488\u003c/p\u003e \u003cp\u003e(0.1676,1.2021)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.2618\u003c/p\u003e \u003cp\u003e(0.1134,0.6024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e0.0017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e0.4191\u003c/p\u003e \u003cp\u003e(0.1348,1.3029)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1329\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.8554(0.7561,4.5527)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.1771\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.8018\u003c/p\u003e \u003cp\u003e(0.6576,4.9375)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.2523\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModel3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e-\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e1.7521\u003c/p\u003e \u003cp\u003e(0.6392,4.8028)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e0.2757\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote: Model1: Use the k-means clustering result as the independent variable and all-cause death as the dependent variable; Model2: Adjust age, gender and disease course on the basis of Model1; Model3: Adjust LVEF and NT-proBNP on the basis of Model2, Heart function classification.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eClustering is a branch of unsupervised learning. It divides samples into groups with similar members. A good clustering algorithm should be efficient, reliable, and able to determine related clusters(\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e) .K-means clustering is an important and popular technique in data mining. This method first uses randomly selected points as the initial centroids (centers), and then updates these centroids in the iterative process until certain convergence criteria are met(\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). The classification of HFpEF patients is complicated by the multidimensional nature of the disease. This study used cluster analysis to describe the four phenotypes of HFpEF patients.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eCluster 1 (young patients with poor cardiac function but preserved renal function)\u003c/h2\u003e \u003cp\u003eThe characteristics of cluster 1 are that the average age is relatively small, the heart function is poor, but the kidney function is preserved. Previous studies have shown that young individuals diagnosed with HFpEF and elderly individuals with HFpEF have similar prevalence of LV filling pressure and LV hypertrophy(\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e). This study found that group 1 had LV diastolic abnormalities, which is the most common cardiac dysfunction in HFpEF. Impaired renal function is common in patients with HF, and the clinical outcome is worse than that of people without impaired renal function. In this group of patients, renal function is preserved, so the incidence of adverse events and the incidence of complications are relatively low.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eCluster 2 (Elderly male patients with cardiorenal insufficiency)\u003c/h2\u003e \u003cp\u003ePhenotype 2 is characterized by older men with poorer cardiac function, the most obvious decline in renal function, higher inflammatory response markers, and the lowest hemoglobin value. Based on these characteristics, the phenotype is similar to one of the previously reported HFpEF phenotypes, namely, elderly patients with renal insufficiency(\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). Most of the progressive decline in renal function observed in HF is thought to be secondary to impaired renal perfusion due to decreased cardiac output(\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Excessive sodium retention caused by renal insufficiency may lead to an increase in LV filling pressure. Chronic high LV filling pressure affects RV diastolic function through ventricular interaction. The LV and RV diastolic function of the second group may be deteriorated through these mechanisms(\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). At the same time, both CKD and HF are in a state of exacerbation of chronic inflammation. CRP is a powerful stimulator of tissue factor production by monocytes (a powerful coagulant). High CRP levels indicate left ventricular dysfunction, cardiac hypertrophy and mortality(\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e), and It plays an active and complex role in the pathophysiology of CRS. Anemia can cause tissue ischemia and peripheral vasodilation, leading to chronic renal vein congestion with progressive nephron loss and interstitial fibrosis. Chronic anemia can also lead to left ventricular hypertrophy and myocardial cell death due to ischemia and necrosis(\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). Compared with heart failure patients with reduced ejection fraction (HFrEF), the poorer prognosis of HFpEF patients may be related to old age, hypertension, anemia, etc(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). Patients with this phenotype are older, have the lowest hemoglobin value, and most of them have hypertension. After adjusting for complications, the overall prognosis is still the worst.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003eCluster 3 (Patients with milder symptoms and shorter illness with preserved heart and kidney function)\u003c/h2\u003e \u003cp\u003eGenerally speaking, LV diastolic capacity decreases with age. Phenotype 3 is younger than phenotype 2 and phenotype 4. Therefore, the morphology and function of LA, RV, and RA are preserved, and the ejection fraction is also higher. The other 3 groups. Age is an independent risk factor for HFpEF. A very small number of HFpEF patients are younger than 65 years old(\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e), and the incidence of HFpEF gradually increases with age(\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e). The 5-year risk of all-cause death in HFpEF patients increases significantly with age. Patients under 55 years old account for 5.7%, and after 85 years, the proportion has reached 47.7%.This group of patients was younger and had better cardiac and renal function, so they exhibited a lower all-cause mortality rate.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003eCluster 4 (Elderly female patients with preserved heart function but poor renal function)\u003c/h2\u003e \u003cp\u003ePhenotype 4 is characterized by older age, the highest proportion of women, higher blood lipid levels, and impaired kidney function. A study of 178 167 Chinese found that women's TC, TG, and LDL-C levels were initially lower than men, but surpassed men in the 51\u0026ndash;55 and 61\u0026ndash;65 age groups(\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e). This may be due to the fact that the blood lipids of men decrease faster with age compared with women(\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e), so the blood lipid levels of elderly women with this phenotype are higher than those of other groups. Based on the results of cross-sectional studies, it is found that HFpEF is mainly a female disease, and the ratio of females to males in hospitalized HFpEF patients is higher(\u003cspan additionalcitationids=\"CR25\" citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e). A follow-up of 8\u0026ndash;10 years found that compared with men, women were not associated with an increased risk of HFpEF(\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e). However, there is renal insufficiency in the reorganized patients, and renal insufficiency is a predictor of readmission and all-cause death in CHF patients, so the incidence of adverse events in this group of patients is second only to the second group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003eAdvantages and Limitations\u003c/h2\u003e \u003cp\u003eThe strengths of our research including a complete HFpEF cohort, using multiple clinical indicators, including detailed phenotypes of heart and kidney functions, and detailed descriptions of basic conditions and complications, and the application of powerful and mature clustering techniques. Our study also has some limitations. First of all, our study has a relatively small sample size, only echocardiographic data, and no detailed phenotypes of arterial structure/function, which results in limitations of the results. In addition, due to the risk of feature overlap, the clustering method we used cannot guarantee to find the best cluster set. However, the principle of the method is relatively simple and the flexibility is high. Therefore, the performance is satisfactory in many cases, and it is still used today widely used.\u003c/p\u003e \u003c/div\u003e"},{"header":"Conclusions","content":"\u003cp\u003eWe used K-means cluster analysis to identify 4 subgroups of HFpEF patients based on common clinical characteristics, and these subgroups have significant differences in the impact on the prognosis of the disease. Old age and renal insufficiency are decisive factors affecting the prognosis. Therefore, it is necessary to pay attention to these two factors for heart failure patients with HFpEF. At the same time, it is necessary to improve the subgroup classification method to better identify HFpEF subgroups, expand the variables included, and prospectively verify our observations.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll experimental procedures were approved by the Ethics Committee of Guang\u0026apos;anmen Hospital, Chinese Academy of Traditional Chinese Medicine [Clinical Ethics Approval Number: 2018-074-KY-01]. The study was conducted in accordance with the Declaration of Helsinki and the International Ethical Guidelines for Human Biomedical Research issued by the International Committee for the Organization of Medical Sciences. All patients provided written informed consent prior to admission to the hospital\u0026quot; .\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\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data and materials in the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNational Natural Science Foundation of China (81904191,82004348), Guang\u0026apos;anmen Hospital of China Academy of Chinese Medical Sciences (2018S420), Capital Health Development Research Special Project (2020-2-4153), Major Public Relations Project of Science and Technology Innovation Project of Chinese Academy of Traditional Chinese Medicine (C12021A01603)\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlasenapp A, Derlin K, Wang Y, Bankstahl M, Meier M, Wollert KC, Bengel FM, Thackeray JT. 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Eur Heart J. 2013;34(19):1424\u0026ndash;31. doi: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/eurheartj/eht066\u003c/span\u003e\u003cspan address=\"10.1093/eurheartj/eht066\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"heart failure with preserved ejection fraction, machine learning, K-means clustering, renal insufficiency, cardiorenal syndrome","lastPublishedDoi":"10.21203/rs.3.rs-3278169/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3278169/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePatients with heart failure with preserved ejection fraction are characterized by high morbidity and poor prognosis. Previous studies have shown that there are several different phenotypes of HFpEF, each with distinct clinical features, and we used k-means clustering to determine the clinical phenotypes of patients with HFpEF and to investigate their impact on prognosis.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe first screened 189 patients with HFpEF who met the inclusion criteria and stratified them using K-mean clustering according to clinical characteristics, routine blood and biochemical parameters, echocardiography, and comorbidities, and determined the optimal number of prime hearts using the error sum of squares. Kaplan-Meier survival curves were then used to assess the impact of each clinical phenotype on all-cause mortality; Cox regression risk models were used to estimate the correlation between each clinical phenotype and long-term prognosis.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eFour HFpEF phenotypes were identified: phenotype 1 was a young patient with poor cardiac function but preserved renal function; phenotype 2 was an older male patient with cardiac and renal insufficiency; phenotype 3 had preserved LA morphology and function, and all patients in this group had higher ejection fractions than the other three groups; phenotype 4 was an older female patient with preserved cardiac function but poor renal function. The Kaplan-Meier survival analysis found that patients with phenotype 2 had significantly lower survival rates than the other three groups, and the Cox proportional risk analysis also found that phenotype 2 showed the highest risk of all-cause mortality (HR\u0026thinsp;=\u0026thinsp;4.6094; 95% CI: 2.0373, 10.4291).\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eK-means cluster analysis classified HFpEF patients into four clinical phenotypes, and the analysis revealed that old age and renal insufficiency were decisive factors affecting prognosis, so the staging and treatment of HFpEF patients should focus on age and renal function.\u003c/p\u003e","manuscriptTitle":"Clinical phenotypic characteristics of heart failure with preserved ejection fraction and its influence on prognosis","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2023-08-29 14:03:29","doi":"10.21203/rs.3.rs-3278169/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fbdacfce-97ca-435e-ab67-83f121c7c9c3","owner":[],"postedDate":"August 29th, 2023","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2023-10-16T14:44:47+00:00","versionOfRecord":[],"versionCreatedAt":"2023-08-29 14:03:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3278169","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3278169","identity":"rs-3278169","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}