Trends of standardized mortality ratio and its correlation with admission patient volume in different intensive care units: A retrospective study from a 12-year multi-center quality improvement project in a metropolitan area

Trends of standardized mortality ratio and its correlation with admission patient volume in different intensive care units: A retrospective study from a 12-year multi-center quality improvement project in a metropolitan area | 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 Trends of standardized mortality ratio and its correlation with admission patient volume in different intensive care units: A retrospective study from a 12-year multi-center quality improvement project in a metropolitan area Yu Qiu, Zhuang Liu, Jing Bai, Mengya Zhao, Haizhou Zhuang, Xiaojun Ji, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3936709/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: Healthcare quality impacts patient prognosis in the intensive care unit (ICU). The healthcare quality can be indicated by the standardized mortality ratio (SMR) and is influenced by the volume of admitted patients. However, the correlation between the admission patient volume and SMR in ICUs remains unclear. Objective: This study examined SMR trends and their influencing factors and assessed the correlation between SMR and the admission patient volume across various ICU types. Methods: We analyzed data retrospectively gathered from 75 ICUs from a Quality Improvement Project from January 2011 to December 2022. It examined the correlations between SMR, admission patient volume, and other quality control indicators. We further compared SMR trends between two groups of ICUs with high or low admission volumes. The study also evaluated inter- and intra-group SMR disparities across hospital levels (secondary versus tertiary) and ICU types (general versus specialty). Results: The study encompassed 425,534 patients. A significant decline in SMR (P<0.001) was observed over the 12 years, alongside a notable negative correlation between admission patient volume and SMR (P<0.001). The low-admission group had a higher SMR than the high-admission group (P=0.010). Both the low (P=0.004) and high admission groups (P=0.001) showed a significant decreasing trend in SMR, with no significant inter-group difference (P=0.267). Moreover, the study identified distinct SMR trends between general ICUs (P=0.018) and secondary hospital ICUs (P=0.048) but not between specialtyICUs (P=0.511) and tertiary hospital ICUs (P=0.276). Conclusion: Over the past 12 years, SMR has significantly decreased. An inverse association was identified between ICU admission patient volume and SMR, with SMR exhibiting considerable variation across different ICU types. These findings underscore the importance of targeted management and healthcare quality enhancement strategies tailored to specific ICU settings. Intensive care unit Standardized mortality ratio Patient volume Healthcare quality improvement Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction The intensive care unit (ICU) caters to patients with critical illnesses, where caregivers are subjected to a high level of stress and partake in complex diagnostic and therapeutic activities daily. This environment renders the ICU a high-risk area for medical errors, hospital-acquired infections, and iatrogenic complications. A comprehensive multi-country cross-sectional survey encompassing 205 ICUs revealed that, among 1,913 patients, 584 encountered adverse events, resulting in an average of 38.8 events per 100 patient days ( 1 ). Similarly, a study conducted in France involving 2,117 critically ill patients reported an adverse event incidence of 16.9% ( 2 ). These alarming statistics underscore the imperative need for precise healthcare quality measurement in the ICU. The ICU patient volume is the number of patients in the ICU. It can help assess the ICU's workload and pressure, reflecting the ICU's diagnostic and treatment capabilities. Previous studies have suggested that the ICU patient volume might affect the quality of ICU care and impact patient prognosis. However, there was no consensus on the association between ICU patient volume and healthcare quality, with some studies suggesting a positive correlation ( 3 , 4 ), whereas other studies suggesting a negative or no correlation ( 5 – 7 ) ( 8 – 10 ). More studies are required to address the relationship between patient volume and ICU healthcare quality. Raw mortality was previously used as an indicator of the quality of healthcare. However, raw mortality does not consider the multiplicity and severity of the illnesses. The standardized mortality ratio (SMR) is an adjusted mortality rate based on the severity of the illness and has been increasingly used as a healthcare quality outcome indicator in various studies ( 11 – 13 ). Flaatten et al. searched 120 quality control indicators in 8 countries and found that SMR was the most commonly used indicator for quality control in ICU ( 14 , 15 ). Kashyap et al. also reported that SMR could be successfully applied in septic shock patients to assess the patient prognosis and healthcare quality control ( 16 ). Healthcare institutions with lower SMRs were generally considered to have better treatment outcomes and higher care ( 17 ). In the present study, we aimed to explore the trend and influencing factors of the SMR and then evaluated the correlation between the SMR and the volume of patients admitted to ICU. Methods Study Design This study was a multi-center retrospective cohort analysis based on data collected from the Quality Improvement Project of the Beijing Intensive Care Medicine Quality Control and Improvement Center, China, spanning 12 years from January 2011 to December 2022. The study protocol received approval from the center's ethics committee and each participating hospital. Due to the retrospective nature of the study design, the requirement for informed consent was waived. Participating intensive care unit This study was part of the Quality Improvement Project of the Beijing Intensive Care Medicine Quality Control and Improvement Center that was officially established in Beijing, China, in 2010. The center monitored and collected data from ICUs that voluntarily participated in the quality improvement program and met specific selection criteria, including 1) having a minimum of 5 beds; 2) having the capability to diagnose and treat critical illnesses, including ventilator-associated pneumonia (VAP), catheter-related bloodstream infections (CRBSI), and catheter-associated urinary tract infections (CAUTI); 3) complying with the equipment, construction, and management requirements of Chinese ICUs ( 18 , 19 ). During the study period, 75 ICUs were included from 67 hospitals, in which 60, 6, and 1 hospitals had one, two, and three ICUs, respectively. Data collections Admission patient volume The admission patient volume is defined as the total number of patients admitted to the ICU within a specific month. This total ICU patient volume encompasses both the patients present in the unit on the first day of the month and all new admissions throughout the month. Importantly, if a patient is admitted to the ICU multiple times within the same month, each admission is counted as a separate instance. Quality control indicators Different quality control indicators have been collected since the initiation of the Quality Improvement Program. In 2011, there were nine indicators, including the unplanned endotracheal extubation rate, reintubation rate within 48 h, ICU re-admission rate within 24 h, incidence of ventilator-associated pneumonia (VAP), incidence of catheter-related bloodstream infections (CRBSI), incidence of catheter-associated urinary tract infections (CAUTI), incidence of pressure ulcer, ICU mortality rate, and SMR. Since 2018, the center started to collect 15 indicators to measure the ICU quality of healthcare as required by the Chinese National Health Commission ( 18 ), which included the proportion of ICU in total inpatients and ICU in total inpatient bed occupancy (%), proportion of APACHE II score ≥ 15 in all ICU patients (%), 3-hour surviving sepsis campaign (SSC) bundle compliance rate, 6-hour SSC bundle compliance rate, microbiology detection before antibiotics rate, proportion of deep venous thrombosis (DVT) prophylaxis, unplanned endotracheal extubation rate, reintubation rate within 48 h , unplanned transfer to ICU rate, ICU re-admission rate within 48 h, incidences of VAP, CRBSI, and CAUTI, expected mortality rate, and SMR (supplementary file 1). Each participating ICU reported the original data to the center, and the center performed the calculations to obtain the value for each indicator. Data collection, reporting, and calculation Each participating ICU had a dedicated trained data collector who collected and submitted the quality control data via the internet ( https://bj.ccmqc.com ) every month. The center reviewed the data and performed range and logic checks. Range checks were conducted to identify inconsistent or out-of-range data and prompt corrections or checks on data entries. Logic checks involve applying predefined logic to identify erroneous or illogical data entries. In addition, the center holds data quality meetings at least twice a year, reviewing all hospital registration records and data and providing feedback on data reporting status every quarter. The detailed data review methods can be found in the supplementary file 2. SMR was calculated as the ratio of actual and expected mortality rates. It was the number of ICU deaths in a month divided by the sum of expected mortality rates for all patients admitted to the ICU. ICU deaths included patients who died in the ICU and those discharged due to irreversible diseases. The expected mortality rates for ICU-admitted patients were calculated based on the Acute Physiology and Chronic Health Evaluation II (APACHE II) score from the worst values of 12 physiological variables within the first 24 h of ICU admission, combined with assessments of the patient's chronic health status and admission diagnosis ( 20 ). Statistical analysis The continuous data were described as mean ± standard error (SE) or median with interquartile range (IQR), depending on the normality test results by the Kolmogorov-Smirnov test. Some continuous data were not in a normal distribution, but using the median and IQR could not adequately represent the data, such as 0.00 (0.00, 0.00). In such cases, these data were presented as both mean ± standard error and median with IQR. Categorical data were described as numbers with frequency and relative frequency (proportion). To investigate the relationship between the admission patient volume and the SMR, we categorized all ICUs into two groups based on the monthly average number of admitted patients using the median as the low and high admission groups. In addition, subgroup analyses were performed based on different hospital characteristics (secondary versus tertiary hospitals) and ICU characteristics (general versus specialty ICUs) (supplementary file 3 Hospital and ICU characteristics). The Student t-test or Mann-Whitney U test was used to compare normally or non-normally distributed continuous data between groups. The Spearman rank correlation coefficient was employed to analyze the correlation and annual trends of various quality control indicators. Furthermore, a multivariate linear regression model was applied to delineate the relationship between the SMR and various quality control indicators. All statistical analyses were conducted using SPSS 26.0 software (IBM, New York, USA). Joinpoint software (version 4.8.0.1) facilitated the analysis of annual trend changes for each indicator, comparing the annual percent change (APC) and the average annual percent change (AAPC) across groups. Additionally, a test for parallelism was conducted to ascertain whether the trends in the two groups were consistent or parallel. All statistical tests were two-tailed, with a P -value of less than 0.05 deemed statistically significant. Results A decreasing trend of SMR over 12 years Over the 12 years, 425,534 patients were admitted in these 75 participating ICUs. The median SMR was 0.324 (0.148, 0.597), and the mean was 0.528 (± 0.013). Over 12 years, there was a significant decreasing trend in the SMR ( P < 0.001) (Fig. 1 ). SMR negatively correlated with admission patient volume We then analyzed the correlation between SMR and other quality control indicators. As SMR was derived from ICU and expected mortality rates, these two indicators were excluded from the analysis. We evaluated 16 quality control-related indicators, including admission patient volume. Table 1 presents the essential characteristics of these indicators and their correlations with SMR. The analysis revealed that the unplanned transfer to ICU ( P = 0.001), ICU re-admission rate within 48 h (24 h ) ( P < 0.001), 3-h SSC bundles compliance rate ( P = 0.026), 6-h SSC bundles compliance rate ( P = 0.027), the proportion of DVT prophylaxis ( P = 0.022), and admission patient volume ( P < 0.001) were negatively correlated with the SMR, while the CRBSI incidence rate ( P < 0.001), CAUTI incidence rate ( P = 0.006), the proportion of ICU in total inpatients ( P < 0.001), and proportion of APACHE II score ≥ 15 in all ICU patients ( P < 0.001) were positively correlated with the SMR. Table 1 Baseline characteristics of quality control-related indicators and their correlations with SMR (Pearson correlation analysis). Variable Mean (SE) Median (IQR) Coefficient P Admission patient volume 78.86(1.33) 52.00(26.00,94.00) -0.283 0.000 Proportion of ICU in total inpatients (%) 2.78(0.08) 2.01(1.06,3.02) -0.010 0.677 Proportion of ICU in total inpatient bed occupancy (%) 2.34(0.08) 1.55(0.98,2.38) 0.146 0.000 Proportion of APACHE II score ≥ 15 in all ICU patients (%) 48.82(0.59) 48.22(24.34,70.73) 0.256 0.000 3 h SSC bundles compliance (%) 88.45(0.58) 100.00(90.00,100.00) -0.053 0.026 6 h SSC bundles compliance (%) 83.80(0.67) 100.00(80.00,100.00) -0.053 0.027 Microbiology detection before antibiotics (%) 75.26(0.66) 89.53(60.00,100.00) 0.043 0.054 Proportion of DVT prophylaxis (%) 74.40(0.60) 87.38(56.24,97.05) -0.050 0.022 Unplanned endotracheal extubation (%) 0.94(0.05) 0.00(0.00,0.00) 0.022 0.140 Reintubation rate within 48 h (%) 2.11(0.09) 0.00(0.00,0.00) -0.016 0.285 Unplanned transfer to ICU (%) 8.53(0.38) 0.00(0.00,7.93) -0.071 0.001 ICU re-admission rate within 48h (24h) (%) 0.63(0.05) 0.00(0.00,0.00) -0.095 0.000 VAP incidence rate (%)/1000 ventilator days 6.29(0.15) 0.00(0.00,8.52) 0.009 0.528 CRBSI incidence rate (%)/1000-line days 2.34(0.07) 0.00(0.00,2.59) 0.086 0.000 CAUTI incidence rate (%)/1000-line days 2.28(0.08) 0.00(0.00,3.09) 0.044 0.006 Pressure sore incidence rate (%) 0.62(0.04) 0.00(0.00,0.00) 0.007 0.717 APACHE II, Acute Physiology and Chronic Health Evaluation II; CAUTI, catheter-associated urinary tract infections; CRBSI, catheter-related bloodstream infections; DVT, deep venous thrombosis; IQR, interquartile range; SE, standard error; SMR, standardized mortality ratio; SSC, surviving sepsis campaign; VAP, ventilator-associated pneumonia. Next, indicators with a P < 0.05 in the correlation analysis were entered as the independent variables into a stepwise multivariate linear regression analysis, with SMR as the dependent variable. The results indicated that the admission patient volume was correlated with the SMR, with a higher number of admitted patients associated with a lower SMR ( P < 0.001) (Table 2 ). In addition, unplanned transfer to ICU was also negatively correlated with the SMR ( P = 0.034). We further analyzed the relationship between admission patient volume and SMR in a scatter plot, which also showed a negative correlation between the mean value of the SMR and the monthly admission patient volume (Fig. 2 ). Table 2 Multivariate linear regression analysis. Variable B Adjusted β P 95% confidence interval Constant 0.662 Admission patient volume -0.001 -0.152 0.000 -0.002, -0.001 Unplanned transfer to ICU(%) -0.003 -0.055 0.034 -0.005, 0.000 Adjusted R-squared = 0.023. Relationship between admission patient volume and SMR depended on the annual patient admission number To further explore the relationship between the number of admitted patients and the SMR, the 75 ICUs were classified into two groups, the low and high admission groups, based on the median of the annual average number of admitted patients. Two group comparisons showed that the SMR was higher in the low and high-admission groups ( P = 0.010) (Table 3 ). The analysis of the annual change trend in SMR suggested that the AAPC significantly decreased in both the low and high admission groups ( P = 0.004, P = 0.001, respectively) but without an inter-group difference ( P = 0.267) (Fig. 3 ). Table 3 Comparisons of SMR between different patients admission groups based on hospital and ICU characteristics. SMR Total N = 75 Low admission group N = 37 High admission group N = 38 P Secondary hospitals N = 27 Median (IQR) 0.385(0.194,0.662) 0.485(0.227, 0.815) 0.307(0.163, 0.488) 0.000 Mean (SE) 0.644(0.031) 0.863(0.054) 0.381(0.014) Tertiary hospitals N = 48 Median (IQR) 0.297(0.133,0.562) 0.486(0.261, 0.778) 0.239(0.112, 0.465) 0.000 Mean (SE) 0.477(0.013) 0.683(0.030) 0.394(0.013) General ICUs N = 53 Median (IQR) 0.374(0.194,0.662) 0.523(0.291, 0.850) 0.300(0.160, 0.513) 0.000 Mean (SE) 0.588(0.017) 0.843(0.036) 0.403(0.009) Specialty ICUs N = 22 Median (IQR) 0.209(0.089,0.437) 0.321(0.167, 0.554) 0.178(0.072, 0.397) 0.000 Mean (SE) 0.394(0.019) 0.466(0.035) 0.371(0.023) TOTAL N = 75 Median (IQR) 0.324(0.148, 0.597) 0.485(0.247, 0.794) 0.256(0.1230.472) 0.010 Mean (SE) 0.528(0.013) 0.766(0.030) 0.391(0.010) IQR, interquartile range; SE, standard error. Comparisons of annual trends in patient admission volume and SMR between different hospital and ICU characteristics ICUs were categorized based on hospital characteristics (secondary versus tertiary hospitals) and ICU characteristics (general versus specialty ICUs). Among the studied ICUs, 27 (36%) were in secondary hospitals, and 48 (64%) in tertiary hospitals. Furthermore, there were 53 (70%) general ICUs compared to 22 (30%) specialty ICUs. Table 3 compares SMR between low and high-admission groups across different hospital and ICU characteristics, revealing significant differences in SMR between the two groups under various conditions. To determine whether the annual change trends in SMR for ICUs with different characteristics were consistent, the joinpoint model was utilized to analyze the APC and AAPC of SMR for both groups, facilitating the detection of parallel trends. After grouping by hospital characteristics, it was found that both the low ( P = 0.003) and the high admission groups ( P = 0.002) showed a significant decrease in AAPC in tertiary hospitals, although there was no inter-group difference ( P = 0.276). In secondary hospitals, the low admission group showed a decreasing trend in APC from 2011 to 2016 ( P = 0.034), with relatively stable changes afterward ( P = 0.515). Nevertheless, the AAPC still exhibited a significant downward trend ( P = 0.032). The high-admission group showed a decreasing trend in AAPC ( P = 0.040). However, when comparing the trends of the two groups, it was found that there was a difference in trends, rejecting the assumption of parallelism ( P = 0.048). (Fig. 4 and supplementary file 4). After grouping based on ICU characteristics, in the general ICUs, the low admission group showed a significant decline in APC from 2011 to 2020 ( P = 0.003), with relatively stable changes from 2020 to 2022 ( P = 0.354). However, this group's AAPC still exhibited a significant downward trend ( P = 0.018). The high admission group in general ICUs showed no significant changes in APC during 2011–2017 ( P = 0.767) and 2020–2022 ( P = 0.058). APC had a downward trend during 2017–2020 ( P = 0.031), but AAPC showed no significant decline ( P = 0.226). The parallelism test indicated that the two groups had different declining trends ( P = 0.018). In the Specialty ICUs, the low-admission group exhibited a non-significant decrease in AAPC ( P = 0.601). The high-admission group showed a significant decrease in AAPC ( P = 0.015). However, the two groups have no significant difference in the changing trend ( P = 0.511). (Fig. 4 and supplementaryfile 4). Discussion Our research showed that the SMR had significantly decreased over the 12 years in the enrolled ICUs. There was a strong link between a higher admission patient volume and a lower SMR. Patients in both low and high-admission groups showed decreasing SMR trends. In the present study, we calculated the SMR based on the expected mortality rate from the APACHE II score. APACHE II is a widely applied scoring system in critical care medicine. It has demonstrated a strong accuracy in predicting patient prognosis ( 20 , 21 ). Our study revealed a median SMR of 0.324 and a mean of 0.528. In previous research, SMRs calculated using the APACHE IV model reached values from 0.4 to 0.89 ( 16 , 22 ), consistently below 1. However, this does not negate the predictive value of the score for prognosis. Many studies used statistical methods to standardize them to avoid overestimating or underestimating expected mortality rates ( 23 – 27 ). Other studies employ composite scores or new predictive models to calculate expected mortality rates ( 11 – 13 , 28 ), increasing the SMR to 1. A study in 21 ICUs from Finland, Estonia, and Switzerland also reported a declining trend in SMR from 2008 to 2017 ( P < 0.001) ( 12 ). This is consistent with the results of our study, indicating a continuous improvement in the overall quality of critical care medicine. In our study, 3h SSC bundles compliance, 6h SSC bundles compliance, and the proportion of DVT prophylaxis were negatively associated with SMR, suggesting a better value in three indicators corresponding to a better prognosis. Studies on quality control indicators from China concluded that process factors need more attention than outcome factors ( 29 ). Unplanned transfer to ICU and ICU re-admission rate within 48h (24h) also negatively correlated with SMR. Some studies suggested that ICU re-admission rate was associated with poorer clinical outcomes ( 30 ). Maharaj et al. conducted a retrospective study of 682,975 patients, arguing that ICU re-admission rate as a performance indicator might not reflect ICU healthcare quality, which supported our result here. This might be because only patients who survived after the initial admission had the possibility for re-admission to the ICU, leading to a selection bias that could mislead ICU performance measurement ( 31 ). We also found that the CRBSI and CAUTI incidence rates were positively correlated with SMR. Although the VAP incidence rate declined, it had no significant correlation with SMR ( P = 0.528). Therefore, the management of VAP will remain a critical quality control focus. Our VAP incidence rate had a median of 0 and an average of 6.29 per 1,000 ventilator days. The incidence rate of VAP in this study was lower than those reported in many studies (8 to 18 per 1000 ventilator days) ( 32 , 33 ). However, compared with another study on the VAP incidence rate in China in 2019 (5.58 per 1000 ventilator days) ( 34 ), there was still room for improvement. The proportion of ICU in total inpatient bed occupancy positively correlated with SMR. Some research results suggested that an increased bed capacity without a proportional increase in medical staff might impact healthcare quality, potentially lowering patient outcomes ( 33 ). ICUs were stratified into two groups based on patient admission volume, with the high-admission group demonstrating a more favorable prognosis ( P < 0.05). ICUs experiencing a greater influx of patients are typically either tertiary hospitals, which boast more beds and a broader patient base, or specialty ICUs, characterized by relatively stable patient sources and shorter average lengths of stay. Such ICUs are likely to have staff possessing superior diagnostic and treatment capabilities, or they may cater to patients presenting with less severe illnesses, both factors contributing to improved clinical outcomes as evidenced by lower SMRs. Research has indicated that in higher-level medical facilities or scenarios where the ICU's share of total inpatient bed occupancy is sufficiently large, an increase in ICU patient volume is correlated with reduced mortality rates ( 3 , 4 , 7 ). In the ICU in the tertiary hospitals, there was no significant difference in the decreasing trend of SMR between the low and high-admission groups ( P = 0.276). This suggested that the improvement in the level of medical care in tertiary hospitals was less influenced by patient admission volume. This was in line with the positioning of tertiary hospitals as comprehensive referral centers providing specialized healthcare services. In contrast, for the ICU in secondary hospitals, both the low and high-admission groups exhibited a decreasing trend with an inconsistent tendency ( P = 0.048), mainly due to a mild SMR decrease in the low-admission group in secondary hospitals from 2016 to 2022. To explain this, we analyzed various indicators in secondary hospitals during 2016–2022 (supplementary file 5). We found a significant increase in the VAP incidence rate in the low admission group while a significant decrease in the VAP incidence rate in the high admission group. This suggested that inadequate infection control for VAP in the low admission group was one of the significant factors influencing SMR. Our study also revealed a significant upward trend in three process indicators for the high admission group, namely 3h SSC bundles compliance, 6h SSC bundles compliance, and microbiology detection before antibiotics. In contrast, there were no improvements in these three indicators in the low-admission group. This highlighted the requirement for SSC bundle adherence and microbiological testing before antibiotics as the critical points for future quality control efforts in the low-admission group. The decreasing trends of the two groups in the general ICUs were not parallel ( P = 0.018). The SMR for the high admission group significantly decreased from 2017 to 2020, likely due to adoption of the sepsis 3.0 guidelines, updated ARDS guidelines, and the National Quality Control Center to enhance quality control indicators and the continuous development and standardization of critical care medicine. From 2020 to 2022, both groups showed a mild upward trend in SMR, although without statistically significant differences. This might be associated with the comprehensive impact of the COVID-19 pandemic in 2020, leading to a surge in critically ill pneumonia patients with a high mortality rate or SMR ( 35 ). The decreasing trend in SMR between the two groups in specialty ICUs showed no significant difference ( P = 0.511), indicating that the specialty ICUs had relatively stable patient sources and were less affected by the COVID-19 pandemic. Our study had certain limitations. Firstly, data on patient demographics and treatments were not collected, which potentially limited its comparisons with other studies and the generalizability of the study results. Secondly, data collection and reporting bias were commonly seen in a retrospective study. Future prospective studies are required to validate our study results. Conclusion Over the past 12 years, from 2011 to 2022, the SMR has significantly decreased, likely attributable to advancements in intensive care medicine and enhancements in healthcare quality. The association between a higher number of patient admissions and a lower SMR emphasizes the importance of concentrated quality improvement initiatives in ICUs experiencing lower patient admissions. The trends in SMR decline between low and high-admission groups varied across different hospitals and ICUs. Notably, in general ICUs and ICUs within secondary hospitals, the declining trends in SMR between the two groups were distinct. Conversely, in specialty ICUs and ICUs in tertiary hospitals, there was no significant difference in the declining trend of SMR between the two groups. This underscores the necessity for tailored management and quality improvement strategies in ICUs with unique characteristics to continuously enhance the quality of intensive care medicine. Abbreviations ICU: intensive care unit; SMR: standardized mortality ratio; APACHE II: Acute Physiology and Chronic Health Evaluation II; VAP: Ventilator-associated pneumonia; CAUTI: Catheter-associated urinary tract infection; CRBSI: Catheter-related bloodstream infection; DVT: Deep vein thrombosis; SSC: surviving sepsis campaign; APC: annual percent change; AAPC: average annual percent change; IQR: interquartile range; SE: standard error; Declarations Ethics Approval: This study does not involve biomedical research on human subjects and is not related to medical ethics Consent for publication: N/A Availability of data and materials: The datasets used and/or analysed during the current study are available from the corresponding author on reasonable request. Competing interests: The authors declare that they have no competing interests. Funding: N/A Author Contribution: XMX, JL and MLD conceived of and designed the study, YQ, ZL and JB collected the data, interpreted the data, and helped draft the manuscript. YQ, JL, MYZ and JFL performed the statistical analysis, interpreted the data, and drafted the manuscript. HZZ, XJJ , YQ, XMX, JL and MLD performed the statistical analysis, interpreted the data, and drafted the revised manuscript. All authors read and approved the final manuscript. Acknowledgments: We thank the medical staff of Beijing Friendship Hospital. We also thank Yu Su for technical support. We thank Medjaden Inc. for assistance with manuscript preparation. References Valentin A, Capuzzo M, Guidet B, Moreno RP, Dolanski L, Bauer P, et al. Patient safety in intensive care: results from the multinational Sentinel Events Evaluation (SEE) study. Intensive Care Med. 2006;32(10):1591–8. Garrouste-Orgeas M, Soufir L, Tabah A, Schwebel C, Vesin A, Adrie C, et al. 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Association of septic shock definitions and standardized mortality ratio in a contemporary cohort of critically ill patients. J Crit Care. 2019;50:269–74. Berthelot S, Lang ES, Quan H, Stelfox HT. Development of a Hospital Standardized Mortality Ratio for Emergency Department Care. Ann Emerg Med. 2016;67(4):517 – 24.e26. Medical quality control indicators for critical care medicine [2015], released by National Health Commission of the People’s Republic China. (In Chinese). Accessed 10 Apr 2015. http://www.nhc.gov.cn/yzygj/s3585/201504/5fa 7461c3d044cb6a93eb6cc6eece087.shtml. Guidelines for the construction and management of critical care medicine [2009], released by China’s Ministry of Health. (In Chinese). Accessed 13 Feb 2009. http://www.nhc.gov.cn/wjw/gfxwj/201304/cc4ffaa8314e4ddab 76788b3f7be8e71.shtml. Knaus WA, Draper EA, Wagner DP, Zimmerman JE. APACHE II: a severity of disease classification system. Crit Care Med. 1985;13(10):818–29. Argyriou G, Vrettou CS, Filippatos G, Sainis G, Nanas S, Routsi C. Comparative evaluation of Acute Physiology and Chronic Health Evaluation II and Sequential Organ Failure Assessment scoring systems in patients admitted to the cardiac intensive care unit. J Crit Care. 2015;30(4):752–7. Kramer AA, Higgins TL, Zimmerman JE. Comparing observed and predicted mortality among ICUs using different prognostic systems: why do performance assessments differ? Crit Care Med. 2015;43(2):261–9. Moreno RP, Metnitz PG, Almeida E, Jordan B, Bauer P, Campos RA, et al. SAPS 3–From evaluation of the patient to evaluation of the intensive care unit. Part 2: Development of a prognostic model for hospital mortality at ICU admission. Intensive Care Med. 2005;31(10):1345–55. Bastos LSL, Wortel SA, de Keizer NF, Bakhshi-Raiez F, Salluh JIF, Dongelmans DA, et al. Comparing continuous versus categorical measures to assess and benchmark intensive care unit performance. J Crit Care. 2022;70:154063. Wortel SA, de Keizer NF, Abu-Hanna A, Dongelmans DA, Bakhshi-Raiez F. Number of intensivists per bed is associated with efficiency of Dutch intensive care units. J Crit Care. 2021;62:223–9. Kovacevic P, Dragic S, Kovacevic T, Momcicevic D, Festic E, Kashyap R, et al. Impact of weekly case-based tele-education on quality of care in a limited resource medical intensive care unit. Crit Care. 2019;23(1):220. Bastos LSL, Hamacher S, Zampieri FG, Cavalcanti AB, Salluh JIF, Bozza FA. Structure and process associated with the efficiency of intensive care units in low-resource settings: An analysis of the CHECKLIST-ICU trial database. J Crit Care. 2020;59:118–23. Endo H, Uchino S, Hashimoto S, Aoki Y, Hashiba E, Hatakeyama J, et al. Development and validation of the predictive risk of death model for adult patients admitted to intensive care units in Japan: an approach to improve the accuracy of healthcare quality measures. J Intensive Care. 2021;9(1):18. Ding X, Ma X, Gao S, Su L, Shan G, Hu Y, et al. Effect of ICU quality control indicators on VAP incidence rate and mortality: a retrospective study of 1267 hospitals in China. Crit Care. 2022;26(1):405. Mulvey HE, Haslam RD, Laytin AD, Diamond CA, Sims CA. Unplanned ICU Admission Is Associated With Worse Clinical Outcomes in Geriatric Trauma Patients. J Surg Res. 2020;245:13–21. Maharaj R, Terblanche M, Vlachos S. The Utility of ICU Readmission as a Quality Indicator and the Effect of Selection. Crit Care Med. 2018;46(5):749–56. Osman S, Al Talhi YM, AlDabbagh M, Baksh M, Osman M, Azzam M. The incidence of ventilator-associated pneumonia (VAP) in a tertiary-care center: Comparison between pre- and post-VAP prevention bundle. J Infect Public Health. 2020;13(4):552–7. He H, Ma X, Su L, Wang L, Guo Y, Shan G, et al. Effects of a national quality improvement program on ICUs in China: a controlled pre-post cohort study in 586 hospitals. Crit Care. 2020;24(1):73. Li Z, Ma X, Gao S, Li Q, Luo H, Sun J, et al. Association between hospital and ICU structural factors and patient outcomes in China: a secondary analysis of the National Clinical Improvement System Data in 2019. Crit Care. 2022;26(1):24. Estimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020-21. Lancet. 2022;399(10334):1513–36. Additional Declarations No competing interests reported. Supplementary Files S1.docx S2.docx S3.docx S4.docx S5.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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3936709","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":272294815,"identity":"8657f93d-6e11-487c-97cc-27fe7a964484","order_by":0,"name":"Yu Qiu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Yu","middleName":"","lastName":"Qiu","suffix":""},{"id":272294816,"identity":"3c33007d-76c1-4f7d-bdd1-bdb49d3c8e3d","order_by":1,"name":"Zhuang Liu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Zhuang","middleName":"","lastName":"Liu","suffix":""},{"id":272294817,"identity":"d9df499c-c00e-4fb0-9ae9-2588ef5ee37a","order_by":2,"name":"Jing Bai","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jing","middleName":"","lastName":"Bai","suffix":""},{"id":272294818,"identity":"16c71d3a-7d89-4172-bf94-b29bb51e5484","order_by":3,"name":"Mengya Zhao","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Mengya","middleName":"","lastName":"Zhao","suffix":""},{"id":272294819,"identity":"c75bd2fb-7707-43b7-a136-e0758bb7db5b","order_by":4,"name":"Haizhou Zhuang","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Haizhou","middleName":"","lastName":"Zhuang","suffix":""},{"id":272294820,"identity":"fa383d97-cc27-466c-8268-8f07ddab143d","order_by":5,"name":"Xiaojun Ji","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiaojun","middleName":"","lastName":"Ji","suffix":""},{"id":272294821,"identity":"db42009d-0ced-4f53-b1a2-68b4cb26115d","order_by":6,"name":"Jingfeng Liu","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jingfeng","middleName":"","lastName":"Liu","suffix":""},{"id":272294822,"identity":"800e2581-e924-4240-ae3b-71ea8178f0b8","order_by":7,"name":"Xiuming Xi","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Xiuming","middleName":"","lastName":"Xi","suffix":""},{"id":272294823,"identity":"f4f1b883-30c6-4e7c-aa75-091faaeef435","order_by":8,"name":"Jin Lin","email":"","orcid":"","institution":"Capital Medical University","correspondingAuthor":false,"prefix":"","firstName":"Jin","middleName":"","lastName":"Lin","suffix":""},{"id":272294824,"identity":"045dc6d3-51af-444f-8c98-4cad318bd3ef","order_by":9,"name":"Meili Duan","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAu0lEQVRIiWNgGAWjYDCCA0D0wMAGymMjVkuCQRqJWhgSGA6ToIXv2uGDBxIKzsvzTztjwPCh7DAD/+wG/Fokb6clAB1223DG7RwDxhnnDjNI3DmAX4sBUCVIS4KBdI4BM2/bYQYDiQRCWvI/ALWcg2j5S5yWHFCIHYBoYSRGC9AvIPXJQL+kFRzsOZfOI3GDgBa+28mPP3z4YyfPPzt544MfZdZy/DMIaEEBB4CYhwT1o2AUjIJRMApwAQBkAEbnrVysuQAAAABJRU5ErkJggg==","orcid":"","institution":"Capital Medical University","correspondingAuthor":true,"prefix":"","firstName":"Meili","middleName":"","lastName":"Duan","suffix":""}],"badges":[],"createdAt":"2024-02-07 11:46:31","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3936709/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3936709/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":51079932,"identity":"e3de621d-4c28-48e5-82ba-a64afcdbb109","added_by":"auto","created_at":"2024-02-13 19:05:52","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":91699,"visible":true,"origin":"","legend":"\u003cp\u003eLine plot of annual trends in standardized mortality ratio(SMR) are presented as means and standard errors. The correlation coefficient was 0.165 (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.001).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3936709/v1/5874dfd603895e47b5ce7f08.jpeg"},{"id":51079934,"identity":"6330ef58-fc33-4f91-92dc-a653ecb6dcf5","added_by":"auto","created_at":"2024-02-13 19:05:52","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":173282,"visible":true,"origin":"","legend":"\u003cp\u003eScatter plot of the the mean value of the SMR corresponding to the admission patient volume. Abbreviation: SMR, standardized mortality ratio.\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3936709/v1/509004b907a0442043086794.jpeg"},{"id":51079935,"identity":"2af428c2-667b-499a-b8f3-aa4c6416daae","added_by":"auto","created_at":"2024-02-13 19:05:52","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":244788,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual change trends in SMR between low and high admission groups. The AAPC significantly decreased in both the low and high admission groups (\u003cem\u003eP=\u003c/em\u003e0.004, \u003cem\u003eP\u003c/em\u003e=0.001, respectively)without significant difference in the downward trend between the two groups (\u003cem\u003eP\u003c/em\u003e =0.267) . Abbreviation: SMR, standardized mortality ratio; AAPC, average annual percent change.\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3936709/v1/75a5663db9c08475c9946cbc.jpeg"},{"id":51079933,"identity":"7246fbf7-c70a-4ac7-95ea-adc0dbc7ffec","added_by":"auto","created_at":"2024-02-13 19:05:52","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":633936,"visible":true,"origin":"","legend":"\u003cp\u003eAnnual percent change in SMR between low admission group (1) and high admissions group (2) in different hospital characteristics (secondary versus tertiary hospitals) and ICU characteristics (general versus specialty ICUs) subgroups. \u003cem\u003eP\u003c/em\u003e is the results from Parallelism test between two groups. * indicates that a significantly different annual percent change (\u003cem\u003eP\u003c/em\u003e\u0026lt;0.05).\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-3936709/v1/05d9a2cde7fe156db992b2ec.jpeg"},{"id":51082142,"identity":"49808549-3bb5-43f9-be0c-6830da72e87f","added_by":"auto","created_at":"2024-02-13 19:21:55","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":669686,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3936709/v1/dd7dc0e1-179a-4be2-9e5f-d96b20433722.pdf"},{"id":51079939,"identity":"92f5cc74-514f-4eef-ac5c-0e1967f5b78e","added_by":"auto","created_at":"2024-02-13 19:05:52","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":15407,"visible":true,"origin":"","legend":"","description":"","filename":"S1.docx","url":"https://assets-eu.researchsquare.com/files/rs-3936709/v1/f664070ef64e57fd12142b4a.docx"},{"id":51079937,"identity":"2cf0d716-8547-483e-ae2a-1998e85b529d","added_by":"auto","created_at":"2024-02-13 19:05:52","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":16360,"visible":true,"origin":"","legend":"","description":"","filename":"S2.docx","url":"https://assets-eu.researchsquare.com/files/rs-3936709/v1/3efc50bce3757f8cabbdbbb5.docx"},{"id":51079941,"identity":"0e19a1d8-14d6-4ae0-821a-345799149685","added_by":"auto","created_at":"2024-02-13 19:05:53","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":13792,"visible":true,"origin":"","legend":"","description":"","filename":"S3.docx","url":"https://assets-eu.researchsquare.com/files/rs-3936709/v1/d997a633c6d4fbf37af1183b.docx"},{"id":51079940,"identity":"d74ed909-17a3-439f-9516-8d5c55ec1eea","added_by":"auto","created_at":"2024-02-13 19:05:52","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":15261,"visible":true,"origin":"","legend":"","description":"","filename":"S4.docx","url":"https://assets-eu.researchsquare.com/files/rs-3936709/v1/a3d628daebd21d6207516a43.docx"},{"id":51080912,"identity":"1717edb0-e77f-433c-8f0d-e35f3f60665b","added_by":"auto","created_at":"2024-02-13 19:13:52","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":15224,"visible":true,"origin":"","legend":"","description":"","filename":"S5.docx","url":"https://assets-eu.researchsquare.com/files/rs-3936709/v1/68383e58f65ff17d5957a6fe.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trends of standardized mortality ratio and its correlation with admission patient volume in different intensive care units: A retrospective study from a 12-year multi-center quality improvement project in a metropolitan area","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe intensive care unit (ICU) caters to patients with critical illnesses, where caregivers are subjected to a high level of stress and partake in complex diagnostic and therapeutic activities daily. This environment renders the ICU a high-risk area for medical errors, hospital-acquired infections, and iatrogenic complications. A comprehensive multi-country cross-sectional survey encompassing 205 ICUs revealed that, among 1,913 patients, 584 encountered adverse events, resulting in an average of 38.8 events per 100 patient days (\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e). Similarly, a study conducted in France involving 2,117 critically ill patients reported an adverse event incidence of 16.9% (\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e). These alarming statistics underscore the imperative need for precise healthcare quality measurement in the ICU.\u003c/p\u003e \u003cp\u003eThe ICU patient volume is the number of patients in the ICU. It can help assess the ICU's workload and pressure, reflecting the ICU's diagnostic and treatment capabilities. Previous studies have suggested that the ICU patient volume might affect the quality of ICU care and impact patient prognosis. However, there was no consensus on the association between ICU patient volume and healthcare quality, with some studies suggesting a positive correlation (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e), whereas other studies suggesting a negative or no correlation (\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e) (\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e). More studies are required to address the relationship between patient volume and ICU healthcare quality.\u003c/p\u003e \u003cp\u003eRaw mortality was previously used as an indicator of the quality of healthcare. However, raw mortality does not consider the multiplicity and severity of the illnesses. The standardized mortality ratio (SMR) is an adjusted mortality rate based on the severity of the illness and has been increasingly used as a healthcare quality outcome indicator in various studies (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e). Flaatten et al. searched 120 quality control indicators in 8 countries and found that SMR was the most commonly used indicator for quality control in ICU (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e). Kashyap et al. also reported that SMR could be successfully applied in septic shock patients to assess the patient prognosis and healthcare quality control (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e). Healthcare institutions with lower SMRs were generally considered to have better treatment outcomes and higher care (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the present study, we aimed to explore the trend and influencing factors of the SMR and then evaluated the correlation between the SMR and the volume of patients admitted to ICU.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Design\u003c/h2\u003e \u003cp\u003e This study was a multi-center retrospective cohort analysis based on data collected from the Quality Improvement Project of the Beijing Intensive Care Medicine Quality Control and Improvement Center, China, spanning 12 years from January 2011 to December 2022. The study protocol received approval from the center's ethics committee and each participating hospital. Due to the retrospective nature of the study design, the requirement for informed consent was waived.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eParticipating intensive care unit\u003c/h3\u003e\n\u003cp\u003e This study was part of the Quality Improvement Project of the Beijing Intensive Care Medicine Quality Control and Improvement Center that was officially established in Beijing, China, in 2010. The center monitored and collected data from ICUs that voluntarily participated in the quality improvement program and met specific selection criteria, including 1) having a minimum of 5 beds; 2) having the capability to diagnose and treat critical illnesses, including ventilator-associated pneumonia (VAP), catheter-related bloodstream infections (CRBSI), and catheter-associated urinary tract infections (CAUTI); 3) complying with the equipment, construction, and management requirements of Chinese ICUs (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e). During the study period, 75 ICUs were included from 67 hospitals, in which 60, 6, and 1 hospitals had one, two, and three ICUs, respectively.\u003c/p\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eData collections\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003eAdmission patient volume\u003c/h2\u003e \u003cp\u003eThe admission patient volume is defined as the total number of patients admitted to the ICU within a specific month. This total ICU patient volume encompasses both the patients present in the unit on the first day of the month and all new admissions throughout the month. Importantly, if a patient is admitted to the ICU multiple times within the same month, each admission is counted as a separate instance.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eQuality control indicators\u003c/h2\u003e \u003cp\u003eDifferent quality control indicators have been collected since the initiation of the Quality Improvement Program. In 2011, there were nine indicators, including the unplanned endotracheal extubation rate, reintubation rate within 48 h, ICU re-admission rate within 24 h, incidence of ventilator-associated pneumonia (VAP), incidence of catheter-related bloodstream infections (CRBSI), incidence of catheter-associated urinary tract infections (CAUTI), incidence of pressure ulcer, ICU mortality rate, and SMR. Since 2018, the center started to collect 15 indicators to measure the ICU quality of healthcare as required by the Chinese National Health Commission (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e), which included the proportion of ICU in total inpatients and ICU in total inpatient bed occupancy (%), proportion of APACHE II score\u0026thinsp;\u0026ge;\u0026thinsp;15 in all ICU patients (%), 3-hour surviving sepsis campaign (SSC) bundle compliance rate, 6-hour SSC bundle compliance rate, microbiology detection before antibiotics rate, proportion of deep venous thrombosis (DVT) prophylaxis, unplanned endotracheal extubation rate, reintubation rate within 48 \u003cem\u003eh\u003c/em\u003e, unplanned transfer to ICU rate, ICU re-admission rate within 48 h, incidences of VAP, CRBSI, and CAUTI, expected mortality rate, and SMR (supplementary file 1). Each participating ICU reported the original data to the center, and the center performed the calculations to obtain the value for each indicator.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eData collection, reporting, and calculation\u003c/h2\u003e \u003cp\u003eEach participating ICU had a dedicated trained data collector who collected and submitted the quality control data via the internet (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://bj.ccmqc.com\u003c/span\u003e\u003cspan address=\"https://bj.ccmqc.com\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003e)\u003c/span\u003e every month. The center reviewed the data and performed range and logic checks. Range checks were conducted to identify inconsistent or out-of-range data and prompt corrections or checks on data entries. Logic checks involve applying predefined logic to identify erroneous or illogical data entries. In addition, the center holds data quality meetings at least twice a year, reviewing all hospital registration records and data and providing feedback on data reporting status every quarter. The detailed data review methods can be found in the supplementary file 2.\u003c/p\u003e \u003cp\u003eSMR was calculated as the ratio of actual and expected mortality rates. It was the number of ICU deaths in a month divided by the sum of expected mortality rates for all patients admitted to the ICU. ICU deaths included patients who died in the ICU and those discharged due to irreversible diseases. The expected mortality rates for ICU-admitted patients were calculated based on the Acute Physiology and Chronic Health Evaluation II (APACHE II) score from the worst values of 12 physiological variables within the first 24 \u003cem\u003eh\u003c/em\u003e of ICU admission, combined with assessments of the patient's chronic health status and admission diagnosis (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eThe continuous data were described as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error (SE) or median with interquartile range (IQR), depending on the normality test results by the Kolmogorov-Smirnov test. Some continuous data were not in a normal distribution, but using the median and IQR could not adequately represent the data, such as 0.00 (0.00, 0.00). In such cases, these data were presented as both mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard error and median with IQR. Categorical data were described as numbers with frequency and relative frequency (proportion).\u003c/p\u003e \u003cp\u003eTo investigate the relationship between the admission patient volume and the SMR, we categorized all ICUs into two groups based on the monthly average number of admitted patients using the median as the low and high admission groups. In addition, subgroup analyses were performed based on different hospital characteristics (secondary versus tertiary hospitals) and ICU characteristics (general versus specialty ICUs) (supplementary file 3 Hospital and ICU characteristics).\u003c/p\u003e \u003cp\u003eThe Student t-test or Mann-Whitney U test was used to compare normally or non-normally distributed continuous data between groups. The Spearman rank correlation coefficient was employed to analyze the correlation and annual trends of various quality control indicators. Furthermore, a multivariate linear regression model was applied to delineate the relationship between the SMR and various quality control indicators. All statistical analyses were conducted using SPSS 26.0 software (IBM, New York, USA). Joinpoint software (version 4.8.0.1) facilitated the analysis of annual trend changes for each indicator, comparing the annual percent change (APC) and the average annual percent change (AAPC) across groups. Additionally, a test for parallelism was conducted to ascertain whether the trends in the two groups were consistent or parallel. All statistical tests were two-tailed, with a \u003cem\u003eP\u003c/em\u003e-value of less than 0.05 deemed statistically significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eA decreasing trend of SMR over 12 years\u003c/h2\u003e \u003cp\u003eOver the 12 years, 425,534 patients were admitted in these 75 participating ICUs. The median SMR was 0.324 (0.148, 0.597), and the mean was 0.528 (\u0026plusmn;\u0026thinsp;0.013). Over 12 years, there was a significant decreasing trend in the SMR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eSMR negatively correlated with admission patient volume\u003c/h2\u003e \u003cp\u003eWe then analyzed the correlation between SMR and other quality control indicators. As SMR was derived from ICU and expected mortality rates, these two indicators were excluded from the analysis. We evaluated 16 quality control-related indicators, including admission patient volume. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents the essential characteristics of these indicators and their correlations with SMR. The analysis revealed that the unplanned transfer to ICU (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.001), ICU re-admission rate within 48 \u003cem\u003eh\u003c/em\u003e (24 \u003cem\u003eh\u003c/em\u003e) (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), 3-h SSC bundles compliance rate (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.026), 6-h SSC bundles compliance rate (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.027), the proportion of DVT prophylaxis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.022), and admission patient volume (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) were negatively correlated with the SMR, while the CRBSI incidence rate (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), CAUTI incidence rate (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.006), the proportion of ICU in total inpatients (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001), and proportion of APACHE II score\u0026thinsp;\u0026ge;\u0026thinsp;15 in all ICU patients (\u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.001) were positively correlated with the SMR.\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\u003eBaseline characteristics of quality control-related indicators and their correlations with SMR (Pearson correlation analysis).\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 \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCoefficient\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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdmission patient volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e78.86(1.33)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e52.00(26.00,94.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.283\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of ICU in total inpatients (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.78(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.01(1.06,3.02)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.677\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of ICU in total inpatient bed occupancy (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.34(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.55(0.98,2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.146\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of APACHE II score\u0026thinsp;\u0026ge;\u0026thinsp;15 in all ICU patients (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e48.82(0.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48.22(24.34,70.73)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3 h SSC bundles compliance (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e88.45(0.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.00(90.00,100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.026\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6 h SSC bundles compliance (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e83.80(0.67)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e100.00(80.00,100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.053\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMicrobiology detection before antibiotics (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e75.26(0.66)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e89.53(60.00,100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.054\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eProportion of DVT prophylaxis (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e74.40(0.60)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e87.38(56.24,97.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnplanned endotracheal extubation (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.94(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00(0.00,0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.140\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eReintubation rate within 48 h (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.11(0.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00(0.00,0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.285\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnplanned transfer to ICU (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e8.53(0.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00(0.00,7.93)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eICU re-admission rate within 48h (24h) (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.63(0.05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00(0.00,0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e-0.095\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVAP incidence rate (%)/1000 ventilator days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e6.29(0.15)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00(0.00,8.52)\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\u003e0.528\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCRBSI incidence rate (%)/1000-line days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.34(0.07)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00(0.00,2.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAUTI incidence rate (%)/1000-line days\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2.28(0.08)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00(0.00,3.09)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.044\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePressure sore incidence rate (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.62(0.04)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.00(0.00,0.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.717\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAPACHE II, Acute Physiology and Chronic Health Evaluation II; CAUTI, catheter-associated urinary tract infections; CRBSI, catheter-related bloodstream infections; DVT, deep venous thrombosis; IQR, interquartile range; SE, standard error; SMR, standardized mortality ratio; SSC, surviving sepsis campaign; VAP, ventilator-associated pneumonia.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eNext, indicators with a \u003cem\u003eP\u0026thinsp;\u0026lt;\u003c/em\u003e\u0026thinsp;0.05 in the correlation analysis were entered as the independent variables into a stepwise multivariate linear regression analysis, with SMR as the dependent variable. The results indicated that the admission patient volume was correlated with the SMR, with a higher number of admitted patients associated with a lower SMR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). In addition, unplanned transfer to ICU was also negatively correlated with the SMR (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.034). We further analyzed the relationship between admission patient volume and SMR in a scatter plot, which also showed a negative correlation between the mean value of the SMR and the monthly admission patient volume (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eMultivariate linear regression analysis.\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=\"\u0026minus;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAdjusted β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cem\u003eP\u003c/em\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% confidence interval\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.662\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdmission patient volume\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.152\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026minus;\" colname=\"c5\"\u003e \u003cp\u003e-0.002, -0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnplanned transfer to ICU(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e-0.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e-0.055\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.005, 0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eAdjusted R-squared\u0026thinsp;=\u0026thinsp;0.023.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eRelationship between admission patient volume and SMR depended on the annual patient admission number\u003c/h2\u003e \u003cp\u003eTo further explore the relationship between the number of admitted patients and the SMR, the 75 ICUs were classified into two groups, the low and high admission groups, based on the median of the annual average number of admitted patients. Two group comparisons showed that the SMR was higher in the low and high-admission groups (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.010) (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). The analysis of the annual change trend in SMR suggested that the AAPC significantly decreased in both the low and high admission groups (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.004, \u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.001, respectively) but without an inter-group difference (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.267) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\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\u003eComparisons of SMR between different patients admission groups based on hospital and ICU characteristics.\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTotal\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;75\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLow admission group\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;37\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eHigh admission group\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;38\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\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\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSecondary hospitals\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.385(0.194,0.662)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.485(0.227, 0.815)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.307(0.163, 0.488)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.644(0.031)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.863(0.054)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.381(0.014)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTertiary hospitals\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.297(0.133,0.562)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.486(0.261, 0.778)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.239(0.112, 0.465)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.477(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.683(0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.394(0.013)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eGeneral ICUs\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.374(0.194,0.662)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.523(0.291, 0.850)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.300(0.160, 0.513)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.588(0.017)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.843(0.036)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.403(0.009)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSpecialty ICUs\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;22\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.209(0.089,0.437)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.321(0.167, 0.554)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.178(0.072, 0.397)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.394(0.019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.466(0.035)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.371(0.023)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eTOTAL\u003c/p\u003e \u003cp\u003eN\u0026thinsp;=\u0026thinsp;75\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMedian (IQR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.324(0.148, 0.597)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.485(0.247, 0.794)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.256(0.1230.472)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.010\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMean (SE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.528(0.013)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.766(0.030)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.391(0.010)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eIQR, interquartile range; SE, standard error.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003eComparisons of annual trends in patient admission volume and SMR between different hospital and ICU characteristics\u003c/b\u003e \u003c/p\u003e \u003cp\u003eICUs were categorized based on hospital characteristics (secondary versus tertiary hospitals) and ICU characteristics (general versus specialty ICUs). Among the studied ICUs, 27 (36%) were in secondary hospitals, and 48 (64%) in tertiary hospitals. Furthermore, there were 53 (70%) general ICUs compared to 22 (30%) specialty ICUs. Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e compares SMR between low and high-admission groups across different hospital and ICU characteristics, revealing significant differences in SMR between the two groups under various conditions. To determine whether the annual change trends in SMR for ICUs with different characteristics were consistent, the joinpoint model was utilized to analyze the APC and AAPC of SMR for both groups, facilitating the detection of parallel trends.\u003c/p\u003e \u003cp\u003eAfter grouping by hospital characteristics, it was found that both the low (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003) and the high admission groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.002) showed a significant decrease in AAPC in tertiary hospitals, although there was no inter-group difference (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.276). In secondary hospitals, the low admission group showed a decreasing trend in APC from 2011 to 2016 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.034), with relatively stable changes afterward (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.515). Nevertheless, the AAPC still exhibited a significant downward trend (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.032). The high-admission group showed a decreasing trend in AAPC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.040). However, when comparing the trends of the two groups, it was found that there was a difference in trends, rejecting the assumption of parallelism (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.048). (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and supplementary file 4).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eAfter grouping based on ICU characteristics, in the general ICUs, the low admission group showed a significant decline in APC from 2011 to 2020 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.003), with relatively stable changes from 2020 to 2022 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.354). However, this group's AAPC still exhibited a significant downward trend (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018). The high admission group in general ICUs showed no significant changes in APC during 2011\u0026ndash;2017 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.767) and 2020\u0026ndash;2022 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.058). APC had a downward trend during 2017\u0026ndash;2020 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.031), but AAPC showed no significant decline (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.226). The parallelism test indicated that the two groups had different declining trends (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018). In the Specialty ICUs, the low-admission group exhibited a non-significant decrease in AAPC (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.601). The high-admission group showed a significant decrease in AAPC (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.015). However, the two groups have no significant difference in the changing trend (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.511). (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e and supplementaryfile 4).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur research showed that the SMR had significantly decreased over the 12 years in the enrolled ICUs. There was a strong link between a higher admission patient volume and a lower SMR. Patients in both low and high-admission groups showed decreasing SMR trends.\u003c/p\u003e \u003cp\u003eIn the present study, we calculated the SMR based on the expected mortality rate from the APACHE II score. APACHE II is a widely applied scoring system in critical care medicine. It has demonstrated a strong accuracy in predicting patient prognosis (\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e). Our study revealed a median SMR of 0.324 and a mean of 0.528. In previous research, SMRs calculated using the APACHE IV model reached values from 0.4 to 0.89 (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e), consistently below 1. However, this does not negate the predictive value of the score for prognosis. Many studies used statistical methods to standardize them to avoid overestimating or underestimating expected mortality rates (\u003cspan additionalcitationids=\"CR24 CR25 CR26\" citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e). Other studies employ composite scores or new predictive models to calculate expected mortality rates (\u003cspan additionalcitationids=\"CR12\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e), increasing the SMR to 1. A study in 21 ICUs from Finland, Estonia, and Switzerland also reported a declining trend in SMR from 2008 to 2017 (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001) (\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e). This is consistent with the results of our study, indicating a continuous improvement in the overall quality of critical care medicine.\u003c/p\u003e \u003cp\u003eIn our study, 3h SSC bundles compliance, 6h SSC bundles compliance, and the proportion of DVT prophylaxis were negatively associated with SMR, suggesting a better value in three indicators corresponding to a better prognosis. Studies on quality control indicators from China concluded that process factors need more attention than outcome factors (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Unplanned transfer to ICU and ICU re-admission rate within 48h (24h) also negatively correlated with SMR. Some studies suggested that ICU re-admission rate was associated with poorer clinical outcomes (\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e). Maharaj et al. conducted a retrospective study of 682,975 patients, arguing that ICU re-admission rate as a performance indicator might not reflect ICU healthcare quality, which supported our result here. This might be because only patients who survived after the initial admission had the possibility for re-admission to the ICU, leading to a selection bias that could mislead ICU performance measurement (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e). We also found that the CRBSI and CAUTI incidence rates were positively correlated with SMR. Although the VAP incidence rate declined, it had no significant correlation with SMR (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.528). Therefore, the management of VAP will remain a critical quality control focus. Our VAP incidence rate had a median of 0 and an average of 6.29 per 1,000 ventilator days. The incidence rate of VAP in this study was lower than those reported in many studies (8 to 18 per 1000 ventilator days) (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e). However, compared with another study on the VAP incidence rate in China in 2019 (5.58 per 1000 ventilator days) (\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e), there was still room for improvement. The proportion of ICU in total inpatient bed occupancy positively correlated with SMR. Some research results suggested that an increased bed capacity without a proportional increase in medical staff might impact healthcare quality, potentially lowering patient outcomes (\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eICUs were stratified into two groups based on patient admission volume, with the high-admission group demonstrating a more favorable prognosis (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). ICUs experiencing a greater influx of patients are typically either tertiary hospitals, which boast more beds and a broader patient base, or specialty ICUs, characterized by relatively stable patient sources and shorter average lengths of stay. Such ICUs are likely to have staff possessing superior diagnostic and treatment capabilities, or they may cater to patients presenting with less severe illnesses, both factors contributing to improved clinical outcomes as evidenced by lower SMRs. Research has indicated that in higher-level medical facilities or scenarios where the ICU's share of total inpatient bed occupancy is sufficiently large, an increase in ICU patient volume is correlated with reduced mortality rates (\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn the ICU in the tertiary hospitals, there was no significant difference in the decreasing trend of SMR between the low and high-admission groups (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.276). This suggested that the improvement in the level of medical care in tertiary hospitals was less influenced by patient admission volume. This was in line with the positioning of tertiary hospitals as comprehensive referral centers providing specialized healthcare services. In contrast, for the ICU in secondary hospitals, both the low and high-admission groups exhibited a decreasing trend with an inconsistent tendency (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.048), mainly due to a mild SMR decrease in the low-admission group in secondary hospitals from 2016 to 2022. To explain this, we analyzed various indicators in secondary hospitals during 2016\u0026ndash;2022 (supplementary file 5). We found a significant increase in the VAP incidence rate in the low admission group while a significant decrease in the VAP incidence rate in the high admission group. This suggested that inadequate infection control for VAP in the low admission group was one of the significant factors influencing SMR. Our study also revealed a significant upward trend in three process indicators for the high admission group, namely 3h SSC bundles compliance, 6h SSC bundles compliance, and microbiology detection before antibiotics. In contrast, there were no improvements in these three indicators in the low-admission group. This highlighted the requirement for SSC bundle adherence and microbiological testing before antibiotics as the critical points for future quality control efforts in the low-admission group.\u003c/p\u003e \u003cp\u003eThe decreasing trends of the two groups in the general ICUs were not parallel (\u003cem\u003eP\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.018). The SMR for the high admission group significantly decreased from 2017 to 2020, likely due to adoption of the sepsis 3.0 guidelines, updated ARDS guidelines, and the National Quality Control Center to enhance quality control indicators and the continuous development and standardization of critical care medicine. From 2020 to 2022, both groups showed a mild upward trend in SMR, although without statistically significant differences. This might be associated with the comprehensive impact of the COVID-19 pandemic in 2020, leading to a surge in critically ill pneumonia patients with a high mortality rate or SMR (\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e). The decreasing trend in SMR between the two groups in specialty ICUs showed no significant difference (\u003cem\u003eP\u0026thinsp;=\u003c/em\u003e\u0026thinsp;0.511), indicating that the specialty ICUs had relatively stable patient sources and were less affected by the COVID-19 pandemic.\u003c/p\u003e \u003cp\u003eOur study had certain limitations. Firstly, data on patient demographics and treatments were not collected, which potentially limited its comparisons with other studies and the generalizability of the study results. Secondly, data collection and reporting bias were commonly seen in a retrospective study. Future prospective studies are required to validate our study results.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003e Over the past 12 years, from 2011 to 2022, the SMR has significantly decreased, likely attributable to advancements in intensive care medicine and enhancements in healthcare quality. The association between a higher number of patient admissions and a lower SMR emphasizes the importance of concentrated quality improvement initiatives in ICUs experiencing lower patient admissions.\u003c/p\u003e \u003cp\u003eThe trends in SMR decline between low and high-admission groups varied across different hospitals and ICUs. Notably, in general ICUs and ICUs within secondary hospitals, the declining trends in SMR between the two groups were distinct. Conversely, in specialty ICUs and ICUs in tertiary hospitals, there was no significant difference in the declining trend of SMR between the two groups. This underscores the necessity for tailored management and quality improvement strategies in ICUs with unique characteristics to continuously enhance the quality of intensive care medicine.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eICU: intensive care unit; SMR: standardized mortality ratio; APACHE II: Acute Physiology and Chronic Health Evaluation II; VAP: Ventilator-associated pneumonia; CAUTI: Catheter-associated urinary tract infection; CRBSI: Catheter-related bloodstream infection; DVT: Deep vein thrombosis; SSC: surviving sepsis campaign; APC: annual percent change; AAPC: average annual percent change; IQR: interquartile range; SE: standard error;\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics Approval:\u0026nbsp;\u003c/strong\u003eThis study does not involve biomedical research on human subjects and is not related to medical ethics\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u0026nbsp;\u003c/strong\u003eN/A\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u0026nbsp;\u003c/strong\u003eThe datasets used and/or analysed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u0026nbsp;\u003c/strong\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003eFunding: N/A\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor Contribution:\u0026nbsp;\u003c/strong\u003eXMX, JL and MLD conceived of and designed the study, YQ, ZL and JB collected the data, interpreted the data, and helped draft the manuscript. YQ, JL, MYZ and JFL performed the statistical analysis, interpreted the data, and drafted the manuscript. HZZ, XJJ , YQ, XMX, JL and MLD performed the statistical analysis, interpreted the data, and drafted the revised manuscript. All authors read and approved the final manuscript.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgments:\u003c/strong\u003e We thank the medical staff of Beijing Friendship Hospital. We also thank Yu Su for technical support. We thank Medjaden Inc. for assistance with manuscript preparation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eValentin A, Capuzzo M, Guidet B, Moreno RP, Dolanski L, Bauer P, et al. Patient safety in intensive care: results from the multinational Sentinel Events Evaluation (SEE) study. 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Association between hospital and ICU structural factors and patient outcomes in China: a secondary analysis of the National Clinical Improvement System Data in 2019. Crit Care. 2022;26(1):24.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eEstimating excess mortality due to the COVID-19 pandemic: a systematic analysis of COVID-19-related mortality, 2020-21. Lancet. 2022;399(10334):1513\u0026ndash;36.\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":"Intensive care unit, Standardized mortality ratio, Patient volume, Healthcare quality improvement","lastPublishedDoi":"10.21203/rs.3.rs-3936709/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3936709/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground:\u003c/strong\u003e Healthcare quality impacts patient prognosis in the intensive care unit (ICU). The healthcare quality can be indicated by the standardized mortality ratio (SMR) and is influenced by the volume of admitted patients. However, the correlation between the admission patient volume and SMR in ICUs remains unclear.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eObjective:\u003c/strong\u003e This study examined SMR trends and their influencing factors and assessed the correlation between SMR and the admission patient volume across various ICU types.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We analyzed data retrospectively gathered from 75 ICUs from a Quality Improvement Project from January 2011 to December 2022. It examined the correlations between SMR, admission patient volume, and other quality control indicators. We further compared SMR trends between two groups of ICUs with high or low admission volumes. The study also evaluated inter- and intra-group SMR disparities across hospital levels (secondary versus tertiary) and ICU types (general versus specialty).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The study encompassed 425,534 patients. A significant decline in SMR (P\u0026lt;0.001) was observed over the 12 years, alongside a notable negative correlation between admission patient volume and SMR (P\u0026lt;0.001). The low-admission group had a higher SMR than the high-admission group (P=0.010). Both the low (P=0.004) and high admission groups (P=0.001) showed a significant decreasing trend in SMR, with no significant inter-group difference (P=0.267). Moreover, the study identified distinct SMR trends between general ICUs (P=0.018) and secondary hospital ICUs (P=0.048) but not between specialtyICUs (P=0.511) and tertiary hospital ICUs (P=0.276).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion:\u003c/strong\u003e Over the past 12 years, SMR has significantly decreased. An inverse association was identified between ICU admission patient volume and SMR, with SMR exhibiting considerable variation across different ICU types. These findings underscore the importance of targeted management and healthcare quality enhancement strategies tailored to specific ICU settings.\u003c/p\u003e","manuscriptTitle":"Trends of standardized mortality ratio and its correlation with admission patient volume in different intensive care units: A retrospective study from a 12-year multi-center quality improvement project in a metropolitan area","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-02-13 19:05:47","doi":"10.21203/rs.3.rs-3936709/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":"c53430ba-3e95-4687-a4a4-52ca305bbcf3","owner":[],"postedDate":"February 13th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-03-19T03:39:21+00:00","versionOfRecord":[],"versionCreatedAt":"2024-02-13 19:05:47","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3936709","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3936709","identity":"rs-3936709","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}