National League stats & predictions

Overview of Volleyball National League Kazakhstan

The Volleyball National League Kazakhstan is a premier sporting event that showcases the best teams and talents in the country. As we approach tomorrow's matches, fans and experts alike are eagerly anticipating thrilling performances and strategic plays. This league not only highlights the competitive spirit of Kazakhstani volleyball but also serves as a platform for athletes to demonstrate their skills on a national stage.

Teams to Watch

• Batyr Astana: Known for their aggressive playstyle and strong defense, Batyr Astana has been a formidable team throughout the season.
• Almaty Eagles: With a focus on precision and teamwork, the Almaty Eagles have consistently delivered impressive performances.
• Aktobe Arlans: Renowned for their dynamic offense, Aktobe Arlans are always a team to watch in crucial matches.

Key Players

Tomorrow's matches feature several standout players who could significantly impact the outcomes. Among them are:

• Alexei Petrov: A seasoned player known for his powerful spikes and strategic game sense.
• Maria Ivanova: A rising star with exceptional serving skills and agility on the court.
• Nikolai Kuznetsov: A versatile player whose defensive prowess is unmatched in the league.

Betting Predictions

As we delve into expert betting predictions for tomorrow's matches, it's important to consider various factors such as team form, head-to-head records, and individual player performances. Here are some insights from top analysts:

Predictions for Key Matches

• Batyr Astana vs. Almaty Eagles: Analysts predict a close match, with Batyr Astana slightly favored due to their recent winning streak.
• Aktobe Arlans vs. Nur-Sultan Titans: Given Aktobe Arlans' offensive strength, they are expected to dominate this encounter.
• Kyzylorda Wolves vs. Pavlodar Panthers: This match is anticipated to be highly competitive, with Kyzylorda Wolves having a slight edge based on home advantage.

Factors Influencing Predictions

Several key factors influence betting predictions:

• Team Form: Recent performances can indicate momentum and confidence levels.
• Injuries: Player availability can drastically alter team dynamics and outcomes.
• Tactical Adjustments: Coaches' strategies and adjustments during games can shift the balance in favor of one team.

Tactics and Strategies

The tactical landscape of volleyball is ever-evolving, with coaches constantly adapting strategies to outmaneuver opponents. In tomorrow's matches, expect to see a variety of tactics that could determine the victors:

Batyr Astana's Defensive Play

• Batyr Astana is renowned for its robust defensive setup. Their ability to quickly transition from defense to offense often catches opponents off guard. Expect them to employ a 'zone defense' strategy aimed at neutralizing Almaty Eagles' attacking threats.
• Their libero will play a crucial role in receiving serves accurately and facilitating smooth transitions into counterattacks.

Almaty Eagles' Precision Passing

• The Almaty Eagles prioritize precision passing over brute force. Their focus on setting up perfect angles for hitters can disrupt Batyr Astana's defensive rhythm if executed flawlessly.
• Their setter will be pivotal in orchestrating plays that exploit any gaps in Batyr Astana’s defense, aiming for quick points through sharp spikes or well-placed tips around blockers.

Aktobe Arlans' Offensive Surge

• Aktobe Arlans thrives on aggressive offensive tactics. They often utilize 'quick sets' designed to catch defenders off-balance before they can establish proper positioning against spikes or blocks.
• This approach requires impeccable timing between setters and hitters but offers high rewards when successful by rapidly accumulating points before defenses can adjust.
• Nikolai Kuznetsov’s role as an outside hitter will be instrumental; his ability to read blockers’ movements allows him to exploit weaknesses effectively.
• Their strategy may involve alternating between power plays led by strong servers like Alexei Petrov who aims directly at opponent vulnerabilities or utilizing deceptive feints mid-spike.
• This multi-faceted offensive strategy keeps opponents guessing while maintaining pressure throughout rallies.

  Possible Counter-Strategies by Opponents

  • To counter Aktobe Arlans’ offensive prowess,
  • Nur-Sultan Titans might deploy an aggressive block formation focusing on intercepting high-speed attacks.
  • An alternative tactic could involve enhancing communication among back-row players to ensure swift coverage during quick sets aiming at weak spots behind primary blockers.

    This layered approach allows them not only defend effectively but also capitalize on counter-attack opportunities whenever Aktobe Arlans overextend themselves trying breaking through initial defenses.

    Past Performance Analysis

    Analyzing past performances provides valuable insights into potential outcomes:

    Batyr Astana vs Almaty Eagles Historical Context

    • In previous encounters between these two teams,
    • Batyr Astana has managed victories primarily through superior blocking techniques which neutralize key attackers from Almaty Eagles.
    • This trend suggests that if Batyr continues prioritizing defensive solidity while exploiting set-up errors made by opponents under pressure, they stand good chances securing wins again.

      Influence of Venue

      • Holding home-court advantage at significant venues like Batyr Stadium boosts morale significantly which translates into enhanced performance metrics across games played there compared with away fixtures. <|diff_marker|> ADD A1000

        This psychological edge often manifests itself through increased energy levels during critical points within matches – contributing positively towards overall results achieved by host teams like Batyr Astana here today.

        Spectator Experience

        Tomorrow’s matches promise an electrifying atmosphere as fans gather either live at stadiums or tune in via broadcasts:

        Venue Atmosphere

        • Volleyball arenas across Kazakhstan are renowned for their passionate crowds whose cheers reverberate throughout events creating unforgettable atmospheres conducive both spectators enjoying live action & athletes performing under heightened conditions driven by fan support. <|diff_marker|> ADD A1000

          This communal experience enhances engagement levels making each game more than just sport – turning it into cultural events cherished deeply within communities supporting respective teams.

          Digital Engagement

          • In addition physical attendance growing digital platforms offer real-time updates interactive features enabling broader audiences worldwide engage remotely following favorite teams progress closely via social media updates live streams analysis commentary etc enriching overall viewing experience beyond traditional means alone.

            Economic Impact

            Volleyball tournaments contribute significantly towards local economies:

            Tourism Boost

            • Cities hosting major league games witness influx tourists drawn not only sports enthusiasts but also those interested exploring local attractions thus stimulating hospitality sectors including hotels restaurants entertainment venues alike. <|diff_marker|> ADD A1000

              This economic stimulation extends beyond immediate vicinity benefiting broader regional markets through increased demand goods services provided catering visitors needs during events timeframe further amplifying positive economic ripple effects generated hosting such prestigious competitions.

              Sponsorship Opportunities

              • Major sponsors leverage visibility associated high-profile sports events aligning brands closely athlete performances enhancing brand recognition amongst target demographics keenly following leagues activities thus maximizing marketing ROI derived sponsoring engagements across various platforms including digital advertising promotional campaigns merchandise sales etc ensuring sustained brand presence amidst evolving consumer landscapes driven sports fandom dynamics.

                Potential Outcomes & Future Prospects

                Tomorrow’s matches hold significant implications not just for standings but also shaping narratives around emerging talents strategic evolutions within league dynamics:

                Predicted Winners & Implications

                • If predictions hold true leading favorites like Batyr Astana secure victories reinforcing dominance trends while underdogs such as Kyzylorda Wolves deliver unexpected upsets challenging established hierarchies thereby adding excitement unpredictability characteristic beloved sporting contests. <|diff_marker|> ADD A1000

                  Evolving strategies witnessed throughout season suggest forthcoming changes tactical approaches adopted teams adapting innovations pioneered successful counterparts ultimately elevating overall quality competition fostering environment where continuous improvement paramount driving future growth potential both domestically internationally recognizing Volleyball National League Kazakhstan pivotal role nurturing talent inspiring generations athletes striving excellence globally recognized arenas.

                  Closing Thoughts From Experts & Analysts 1: DOI:1010.1186/s12931-022-02050-y 2: # The effect of glucocorticoids treatment duration on mortality risk among patients hospitalized with COVID-19: results from a retrospective cohort study using data from MIMIC IV database 3: Authors: Hengshuang Xie, Yuxuan Wang, Zhenyu Yang, Xinyu Zhang, Yuwei Liang, Dongdong Wang, Jie Liang, Tao Chen, Yunpeng Chen, Rui Liang Shaoxiong Liu Qiang He Jinzhuo Chen Guangming He Yi Chen Xiaoyan Zhou Hongxin Chen Xiaoyan Zhou Changchun He Chunfeng Lu Wei Zhang Xiang Song Liankui Jiang Yan Wang Chao Yu Long Huang Ming Yang Jianxiang Feng Yuan Zhang Chenggang Liu Yunbo Yang Shuai Wang Yongjiang Ma Guangtao Zhou Jianwen Cao Guohua Dai Xu Zhu Ying Zhao Pei Zhao Bin Shi Guoliang Deng Jingfei Wu Zhiying Ye Junyan Liu Yan Wang Ping Lin Yongqiang Pan Mingxia Peng Zheng Fang Yibin Li Changqing Li Hui Xu Xiaoqi Huo Fei Huang Zhongjun Lv Lei Luo Zhengyu Song Jianping Zhao Yan Gao Yu Huang Mingyang Ke Xiang Zhong Ling Fan Dian Qi Niu Ying Shi Qing Ma Honglin Xu Yue Shi Dan Zhang Jiaxing Zuo Qin Sun Min Quan Ling Kai Xu Xin Chen Hongmei Hou Yongyi Yao Huijuan Liu Jing Cai Wei Wei Qiao Meng Tianle Ye Biao Yi Zhang Jingjing Xu Shiyuan Wang Zhiqiang Su Bo Peng Yan Wang Hua Zhang Jiajia Liu Chuanbin Fu Shaofei Kong Wenjia Huang Xiuhua Sun Tongtong Zheng Jiaqi Yin Tingting Yao Tingting Li Lingbo Shen Wenhua Pan Ning Zhang Ruixue Liu Xinran Tan Yuhang Pan Yuefeng Wu Yuanyuan Lin Jingwen Lin Zhiliu Guo Fei Bi Haiyun Bai Yaobin Xiao Wenlong Yu Yifan Jiang Yanbin Fu Jialin Dai Mengmeng Peng Haoqiang Huang Wenyu Dong Tianyi Hu Haiyang Yang Ziyu Zhu Qian Kang Xiao Juan Wei Jiayi Tang Junjie Wu Lingling Jiang Meijun Zhao Jiajia Sun Siyu Jiang Xinyu Zhao Chunchun Du Xiaodong Cheng Ruiyu Zhu Chengcheng Shen Mengchen Feng Miaoxin Gao Yingping Feng Xiaojun Fu Haoran Wang Jiahui Gao Shuai Qin Zhenning Yang Tiantian Ni Lili Liao Jiawei Gao Na Wanbao Li Wanbao Qin Yi Ding Hang Zhao Weihong Zhou Pengpeng Yu Baohua He Bin Zhuo Xinran Dong Qiuyue Lu Qian Han Qing Lin Yongxue Tu Haocheng Wei Hanmin Wu Qiuyue Lu Qian Han Qing Lin Yongxue Tu Haocheng Wei Hanmin Wu Qiuyue Lu Qian Han Qing Lin Yongxue Tu Haocheng Wei Hanmin Wu Guozhang Liang Fuming Gong Shengnan Sun Ruirui Tang Minyan Ni Ruirong Gou Pengfei Jiang Ruinan Mo Lixin Zhang Runze Kang Mingxin Fang Congyi Song Mengmeng Shen Yuanhan Luo Junjie Deng Wenbin Fu Zhengyu Song Jianping Zhao Yan Gao Yu Huang Mingyang Ke Xiang Zhong Ling Fan Dian Qi Niu Ying Shi Qing Ma Honglin Xu Yue Shi Dan Zhang Jiaxing Zuo Qin Sun Min Quan Ling Kai Xu Xin Chen Hongmei Hou Yongyi Yao Huijuan Liu Jing Cai Wei Wei Qiao Meng Tianle Ye Biao Yi Zhang Jingjing Xu Shiyuan Wang Zhiqiang Su Bo Peng Yan Wang Hua Zhang Jiajia Liu Chuanbin Fu Shaofei Kong Wenjia Huang Xiuhua Sun Tongtong Zheng Jiaqi Yin Tingting Yao Tingting Li Lingbo Shen Wenhua Pan Ning Zhang Ruixue Liu Xinran Tan Yuhang Pan Yuefeng Wu Yuanyuan Lin Jingwen Lin Zhiliu Guo Fei Bi Haiyun Bai Yaobin Xiao Wenlong Yu Yifan Jiang Yanbin Fu Jialin Dai Mengmeng Peng Haoqiang Huang Wenyu Dong Tianyi Hu Haiyang Yang Ziyu Zhu Qian Kang Xiao Juan Wei Jiayi Tang Junjie Wu Lingling Jiang Meijun Zhao Jiajia Sun Siyu Jiang Xinyu Zhao Chunchun Du Xiaodong Cheng Ruiyu Zhu Chengcheng Shen Mengchen Feng Miaoxin Gao Yingping Feng Xiaojun Fu Haoran Wang Jiahui Gao Shuai Qin Zhenning Yang Tiantian Ni Lili Liao Jiawei Gao Na Wanbao Li Wanbao Qin Yi Ding Hang Zhao Weihong Zhou Pengpeng Yu Baohua He Bin Zhuo Xinran Dong Qiuyue Lu Qian Han Qing Lin Yongxue Tu Haocheng Wei Hanmin Wu Qiuyue Lu Qian Han Qing Lin Yongxue Tu Haocheng Wei Hanmin Wu Guozhang Liang Fuming Gong Shengnan Sun Ruirui Tang Minyan Ni Ruirong Gou Pengfei Jiang Ruinan Mo Lixin Zhang Runze Kang Mingxin Fang Congyi Song Mengmeng Shen Yuanhan Luo Junjie Deng Wenbin Fu Zhengyu Song Jianping Zhao Yan Gao Yu Huang Mingyang Ke Xiang Zhong Ling Fan Dian Qi Niu Ying Shi Qing Ma Honglin Xu Yue Shi Dan Zhang Jiaxing Zuo Qin Sun Min Quan Ling Kai Xu Xin Chen Hongmei Hou Yongyi Yao Huijuan Liu Jing Cai Wei Wei Qiao Meng Tianle Ye Biao Yi Zhang Jingjing Xu Shiyuan Wang Zhiqiang Su Bo Peng Yan Wang Hua Zhang Jiajia Liu Chuanbin Fu Shaofei Kong Wenjia Huang Xiuhua Sun Tongtong Zheng Jiaqi Yin Tingting Yao Tingting Li Lingbo Shen Wenhua Pan Ning Zhang Ruixue Liu Xinran Tan Yuhang Pan Yuefeng Wu Yuanyuan Lin Jingwen Lin Zhiliu Guo Fei Bi Haiyun Bai Yaobin Xiao Wenlong Yu Yifan Jiang Yanbin Fu Jialin Dai Mengmeng Peng Haoqiang Huang Wenyu Dong Tianyi Hu Haiyang Yang Ziyu Zhu Qian Kang Xiao Juan Wei Jiayi Tang Junjie Wu Lingling Jiang Meijun Zhao Jiajia Sun Siyu Jiang Xinyu Zhao Chunchun Du Xiaodong Cheng Ruiyu Zhu Chengcheng Shen Mengchen Feng Miaoxin Gao Yingping Feng Xiaojun Fu Haoran Wang Jiahui Gao Shuai Qin Zhenning Yang Tiantian Ni Lili Liao Jiawei Gao Na Wanbao Li Wanbao Qin Yi Ding Hang Zhao Weihong Zhou Pengpeng Yu Baohua He Bin Zhuo Xinran Dong Qiuyue Lu Qian Han Qing Lin Yongxue Tu Haocheng Wei Hanmin Wu Zi Jin Mai Mei Ju Mei Ju Mei Ju Mei Ju Mei Ju Mei Ju Mei Ju Mei Ju Mei Ju Mei Ju Mei Ju Mei Ju Zi Jin Mai Mei Ju Mai Zi Jin Mai Zi Jin Mai Zi Jin Mai Zi Jin Mai Zi Jin Mai Zi Jin Mai Zi Jin Mai Zi Jin Mai Zi Jin Mai Zi Jin Main Text: ## Abstract 4: **Background:** Glucocorticoids (GCs) have been widely used during hospitalization for COVID-19 patients; however whether GC treatment duration affects patient prognosis remains unclear. 5: **Methods:** Data was extracted from Medical Information Mart for Intensive Care IV (MIMIC IV). Patients were divided into three groups according to GC treatment duration (< 7 days; ≥ 7 days; no GC treatment). Kaplan–Meier survival curves were plotted using log-rank tests. Cox proportional hazard regression was used after adjusting confounding variables. 6: **Results:** Overall mortality was lower in patients treated with GCs compared with those without GC treatment (28% vs.32%; p = 0.014). Patients treated with GCs less than seven days had lower mortality rates than those treated longer than seven days (24% vs.33%; p = 0.005). In multivariate analysis adjusted confounding variables including age (HR = 1.03 [95% CI = 1–1·04]; p = 0·035), Charlson Comorbidity Index score (HR = 1·11 [95% CI = 1·07–1·16]; p < 0·001), blood urea nitrogen (HR = 1·01 [95% CI = 1–1·02]; p = 0·008), lactic acid level (HR = 1·08 [95% CI = 1·02–1·15]; p = 0·009), sequential organ failure assessment score (HR = 1·27 [95% CI = 1·23–1·32]; p ≤ 0 ·001), vasopressor use (HR = 9 ·89 [95% CI = 6 ·79–14 ·44]; p ≤ 0 ·001), invasive mechanical ventilation use (HR = 17 ·34 [95% CI = 12 ·48–24 ·13]; p ≤ 0 ·001) showed higher mortality risk among patients treated with GCs longer than seven days compared with those treated less than seven days. 7: **Conclusions:** Our study showed that COVID-19 patients treated with GCs had lower mortality rates compared without GC treatment; however longer duration (>7d) was associated with higher mortality risk compared shorter duration (<7d). 8: **Supplementary Information:** The online version contains supplementary material available at https://doi.org/10.1186/s12931-022-02050-y. 9: ## Background 10: Coronavirus disease of 2019(COVID-19) caused by severe acute respiratory syndrome coronavirus type two(SARS-CoV-2) has become one of most serious public health concerns since December last year [[1]]. At present there is no specific drug therapy recommended by WHO guidelines [[2]] except glucocorticoids(GCs). 11: Since March last year many studies have shown that systemic corticosteroid therapy reduced mortality rate among critically ill COVID-19 patients [[3]– [5]]. Therefore corticosteroid therapy has become one of major therapies recommended by many countries [[6], [7]]. 12: However whether corticosteroid therapy benefits all COVID-19 patients remains unclear because there were still some reports suggesting corticosteroid therapy may increase complications such as secondary infection [[8]], delayed viral clearance [[9]], gastrointestinal bleeding [[10]], osteoporosis [[11]], hyperglycemia [[12]], myopathy [[13]] especially when administered long term. 13: Although guidelines recommend short-term administration(7d) [[14], [15]], some studies suggested prolonged administration may benefit certain population such as critically ill patients[[16], [17]]. 14: Therefore we conducted this study using data from MIMIC IV database trying to assess whether different durations of corticosteroid therapy affect patient prognosis. 15: ## Methods 16: ### Study design and data source 17: Data was extracted from Medical Information Mart for Intensive Care IV(MIMIC IV)[[18]]. All data extraction procedures were performed using PostgreSQL software(version13). The authors completed training course successfully obtaining certification needed before accessing MIMIC IV database(Completion certificate number included). 18: ### Study population inclusion criteria 19: We identified all adult patients hospitalized due to SARS-CoV-2 infection between January2020andMarch2021using ICD codeU07•09andU07•92as criteria[[19]] excluding pediatric population(<18years old). 20: ### Variables definition 21: Demographic characteristics included age(gender was excluded due to equal distribution between groups); medical history included hypertension(diagnosis codes401•00to401•99or405•00to405•99); diabetes mellitus(diagnosis codes250•00to250•93); coronary heart disease(diagnosis codes410•00to414•90); chronic obstructive pulmonary disease(diagnosis codes490•00to496•90); Charlson Comorbidity Index(we calculated Charlson Comorbidity Index based on medical history listed above). 22: Clinical parameters included body mass index(BMI)(calculated according formula weight(kg)/height(m)^{−^} ^{^} ^{^} ^{^} ^{^} ^{^} ^{^} ^{^} ^{^} ^{^} _{}); systolic blood pressure(SBP)(mmHg); diastolic blood pressure(DBP)(mmHg); heart rate(bpm); body temperature(°C); white blood cell count(WBC)(×10^{−^} ^{^} ^9 /L); lymphocyte count(LY)(×10^{−^} ^9 /L); platelet count(PCT)(×10^{−^} ^9 /L); hemoglobin(HGB)(g/L); albumin(ALB)(g/L); prothrombin time(PT)(S); activated partial thromboplastin time(APTT)(S); international normalized ratio(INR)(*n*/*n*/*n*/*n*/*n*/*n*/*n*/*n*/*n*/*n*/*n*/*n*/_{} ); creatinine(Cr)(umol/L); blood urea nitrogen(BUN)(mmol/L); lactic acid(LA)(mmol/L). 23: Laboratory examination included serum creatinine(SCr)(umol/L): SCr≥133μmol/L indicates abnormality[[20]]; alanine transaminase(AST)≥40U/L indicates abnormality[[21]]; total bilirubin(TBil)>17μmol/L indicates abnormality[[22]]; creatine kinase(CK)>170U/L indicates abnormality[[23]]; troponin(Troponin I)>26ng/mL indicates abnormality[[24]]; lactate dehydrogenase(LDH)>240U/L indicates abnormality[[25]]; d-dimer(DD)>500ng/mL indicates abnormality[[26]]; interleukins(IL)-6(IL6)>7pg/mL indicates abnormality[[27]]. 24: Severity indicators included sequential organ failure assessment score(SOFA score)[[28]] Acute Physiology Score(APACHE II)[[29]] Simplified Acute Physiology Score(SAPS II)[[30]] Extracorporeal membrane oxygenation(ECMO)[[31]] invasive mechanical ventilation(IMV)[[32]] non-invasive mechanical ventilation(NIV)[[33]] vasopressor use(Vasopressor use includes norepinephrine,NOR; dopamine,DOPA; epinephrine,EPI; phenylephrine,PHE; vasopressin,VAS; metaramine,META)[[34]]. Outcome indicator was all cause death within hospitalization period(Including death due natural causes or iatrogenic reasons.) 25: ### Exposure variable definition 26: Exposed variable defined as methylprednisolone(MPS) dose administrated per day(calculated according formula total MPS dose administrated/total administration days).We classified exposed variable into three groups(<7d≤14d≥14d)according daily MPS dose administrated(we chose this classification method because many guidelines recommended MPS should be administrated less than seven days[[35]]). 27: ### Statistical methods 28: Continuous variables were expressed as mean ± standard deviation(*SD*) or median(interquartile range[*IQR*]) according normal distribution or skewed distribution respectively determined using Shapiro-Wilk test.[[36]] 29: Differences between continuous variables were analyzed using *t*-test or Mann Whitney *U*-test according normal distribution or skewed distribution respectively.[[37]] 30: Differences between categorical variables were analyzed using chi-square test.[[38]] 31 : Kaplan-Meier survival curves were plotted comparing cumulative survival rate between groups using log-rank tests.[[39]] 32 : Cox proportional hazards model was used analyzing hazard ratio(*HR*)for all cause death comparing different durations of exposure variable after adjusting confounding variables.[[40]] 33 : Statistical analyses were performed using SPSS software(version25)and GraphPad Prism software(version8). 34 : Two-sided *P*-value<0 .05 considered statistically significant difference. 35 : ## Results 36 : ### Patient characteristics stratified by different durations of exposure variable(MPS) 37 : Overall we enrolled1208patients meeting inclusion criteria.Of these1208patients130received no MPS treatment(a total of91patients received other types of steroids but not MPS so we excluded these patients),705received MPS less than seven days,a total of373received MPS longer than seven days.Figure Sla shows flowchart detailing study enrollment process.The demographic characteristics,major medical history,and clinical parameters stratified by different durations(exposure variable[MPS])were shown in Table Sla,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a′b′c′d′e′f′g′ respectively.In general age Charlson Comorbidity Index score SOFA score APACHE II score SAPS II score SBP DBP heart rate body temperature WBC LY PCT HGB ALB PT APTT INR Cr BUN LA IL6 TBil AST CK Troponin DD ECMO IMV NIV NOR DOPA EPI PHE VAS META usage rates differed significantly among three groups(p values ranged from<0 .001to≤0 .05). 38 : ### Mortality rate stratified by different durations(exposure variable[MPS]) 39 : Overall mortality rate was lower among group treated with MPS compared without MPS treatment(28%vs32%; *p*=0 .014).(Figure Slb shows Kaplan-Meier survival curves comparing cumulative survival rates among three groups.)Mortality rate decreased gradually along increase daily dose administered(MPS dose≤20mg/day:Mortality Rate=26%;MPS dose >20mg/day:Mortality Rate=28%).Mortality rate decreased gradually along decrease administration days(MPS duration≤5days:Mortality Rate=22%;MPS duration >5days:Mortality Rate=30%). 40 : Patients treated with MPS less than seven days had lower mortality rates compared those treated longer than seven days(24 %vs33 %; *p*=0 .005).(Figure Slc shows Kaplan-Meier survival curves comparing cumulative survival rates between two groups.)In univariate analysis adjusted confounding variables including age(Charlson Comorbidity Index score SOFA score APACHE II score SAPS II score SBP DBP heart rate body temperature WBC LY PCT HGB ALB PT APTT INR Cr BUN LA IL6 TBil AST CK Troponin DD ECMO IMV NIV NOR DOPA EPI PHE VAS META usage rates showed higher mortality risk among group treated longer than seven days compared those treated less than seven days(Table Slc,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a′b′c′d′e′f′ respectively).(Figure Sl shows forest plot displaying HRs along corresponding *CI*s.) 41 : In multivariate analysis adjusted confounding variables including age(Charlson Comorbidity Index score SOFA score APACHE II score SAPS II SBP DBP heart rate body temperature WBC LY PCT HGB ALB PT APTT INR Cr BUN LA ECMO IMV NIV NOR DOPA EPI PHE VAS META usage rates showed higher mortality risk among group treated longer than seven days compared those treated less than seven days(age HR = 1 .03 [*CI*= 1 –1 ·04]; *p*=0 .035;Charlson Comorbidity Indexscore HR = 1 .11 [*CI*= 1 ·07–1 ·16]; *p<*<0 .001;SOFAscore HR = 1 .27 [*CI*= 1 ·23–131 ; *p<*<0 .001;APACHEIIscore HR = 102 [*CI*=97 –108]; *p<*<00001 ;SAPSIIscore HR =101 [*CI*=98 –104]; *p<*<00001 ;SBP HR =99 [*CI*=98 –99 ]; *p=*007 ;DBP HR =99 [*CI*=98 –99 ]; *p=*002 ;heartrate HR =100 [*CI*=100 –101 ]; *p=*002 ;bodytemperature HR =98 [*CI*=96 –99 ]; *p=*003 ;WBC HR =101 [*CI*=100 –102 ]; *p=*003 ;LY HR *=*[96]*[*92*-99]*[*-*]*[*-*]*[*-*]*[*-*]*[*-*]*[*-*]*[*-*]*[*-*]*[*-*]*[*-*]*[*-*][***−***]***−***][***−***]***−***][***−***]***−***][***(*****‐*****)***(*****‐*****)***(*****‐*****)***(*****‐*****)***(*****‐*****)***(*****‐*****)***(*****‐*****)***(*****‐*****)***(*****‐*****)***(*****‐*****)]** (*CI*[91—103]; ***P***************=*.*002)*,* ***HGB**************=*.*004)*,* ***ALB*************=*.*005)*,* ***PT*************=*.*009)*,* ***LA*************=*.*009)*,* ***ECMO***************<*.*001)*,* ***IMV***************<*.*001)*,* ***NIV***************=<*.00001 )*,* ***VAS***************=<*.00001 )*,* ***NOR***************=<*.00001 ) *(Table Sla,b,c,d,e,f,g,h,i,j,k,l,m,n,o,p,q,r,s,t,u,v,w,x,y,z,a'b"c'd'e"f"g" respectively).* 42 : *(Figure Sl shows forest plot displaying adjusted* 43: 44 Fig Sla Forest plot displaying hazard ratios(*HR*s)along corresponding confidence intervals(*CIs*) 45 Fig Slb Kaplan-Meier curve displaying cumulative survival proportion comparing overall cohort(*N*=1208) 46 Fig Slc Kaplan-Meier curve displaying cumulative survival proportion comparing group receiving glucocorticoids (*N*=1078) 47 Fig Sld Forest plot displaying unadjusted hazard ratios(*HR*s)along corresponding confidence intervals(*CIs*) 48 Fig Sle Forest plot displaying unadjusted hazard ratios(*HR*s)along corresponding confidence intervals(*CIs*) 49 Fig Slf Forest plot displaying unadjusted hazard ratios(*HR*s)along corresponding confidence intervals(*CIs*) 50 Fig Slg Forest plot displaying unadjusted hazard ratios(*HR*s)along corresponding confidence intervals(*CIs*) 51 Fig Slh Forest plot displaying unadjusted hazard ratios(*HR*s)along corresponding confidence intervals(*CIs*) 52 Fig Sil Forest plot displaying unadjusted hazard ratios(*HR*s)along corresponding confidence intervals (*CIs*) 53 Fig Sm Forest plot displaying unadjusted hazard ratios (*HR*s) along corresponding confidence intervals (*CIs*) 54 Fig Sn Forest plot displaying unadjusted hazard ratios (*HR*s ) along corresponding confidence intervals (*CIs*) 55 Fig So Forest plot displaying unadjusted hazard ratios (*HR*s ) along corresponding confidence intervals (*CIs*) 56 Fig Sp Forest plot displaying unadjusted hazard ratios (*HR*s ) along corresponding confidence intervals (*CIs*) 57 Fig Sq Forest plot displaying unadjusted hazard ratios (*HR*s ) along corresponding confidence intervals (*CIs*) 58 Fig Sr Forest plot displaying unadjusted hazard ratios (*HR*s ) along corresponding confidence intervals (*CIs*) 59 Fig Ss Forest plot displaying unadjusted hazard ratios (*HR*s ) along corresponding confidence intervals (*Cis* 60 Tab le Sl Demographic characteristics stratified by different durations(exposure variable[MPS]) 61 Tab le Sm Major medical history stratified by different durations(exposure variable[MPS]) 62 Tab le Sn Clinical parameters stratified by different durations(exposure variable[MPS]) 63 Tab le So Laboratory examination stratified by different durations(exposure variable[MPS]) 64 Tab le Sp Severity indicators stratified by different durations(exposure variable[MPS]) 65 Tab le Sq Outcome indicator stratified by different durations(exposure variable[MPS]) 66 Tab le Sr Mortality rate stratified by different durations(exposure variable[MPS]) 67 Tab le St Univariate analysis showing adjusted confounding variables indicating higher mortality risk associated longer exposure duration(Methyprednisolon*e*>7*d* 68 Tab le Su Multivariate analysis showing adjusted confounding variables indicating higher mortality risk associated longer exposure duration(Methyprednisolon*e*>7*d* 69 Tab le Sv Hazard ratio comparison showing adjusted confounding variables indicating higher mortalit*y*risk associated longer exposure duration(Methyprednisolon*e*>7*d* 70 Tab le Sw Hazard ratio comparison showing adjusted confounding variables indicating higher mortalit*y*risk associated shorter exposure duration(Methyprednisolon*e*>7*d* 71 Tab le Sx Hazard ratio comparison showing adjusted confounding var*i*a*b*l*e*s indicating higher mortalit*y*risk associated Methyprednisolon*e*>*20mg/d* 72 Tab le Sy Hazard ratio comparison showing adjusted confounding var*i*a*b*l*e*s indicating lower mortalit*y*risk associated Methyprednisolon*e*>*20mg/d* 73 Tab le Sz Hazard ratio comparison showing adjusted confounding var*i*a*b*l*e*s indicating lower mortalit*y*risk associated Methyprednisolon*e*>14*d* 74 Tab le Sa'b'Hazard ratio comparison showing adjusted confounding var*i*a*b*l*e*s indicating lower mortalit*y*risk associated Methyprednisolon*e*>14*d* 75 ## Discussion 76 : Our study indicated that although overall COVID-19 patients receiving glucocorticoids had better prognosis compared those without glucocorticoids treatment,longer glucocorticoids treatment duration resulted worse outcome rather improved prognosis.Multiple studies have demonstrated benefits related short-term glucocorticoids therapy especially critically ill COVID-19 patients [[41]– [43]].Our study supported this conclusion.Conversely other studies suggested long-term glucocorticoids therapy may improve prognosis even non-critically ill population.Hence our study findings did not support this conclusion.Therefore our findings emphasized importance selecting appropriate patient population when considering long-term glucocorticoids therapy.Consistent with our findings many studies have reported complications related long-term glucocorticoids therapy especially secondary infection delay viral clearance gastrointestinal bleeding osteoporosis hyperglycemia myopathy hence physicians should carefully weigh risks versus benefits before deciding whether long-term glucocorticoids therapy should be implemented.Similarly another retrospective cohort study found that although COVID-19 patients receiving hydrocortisone had better prognosis compared those without hydrocortisone treatment,longer hydrocortisone treatment resulted worse outcome rather improved prognosis consistent with our findings hence physicians should carefully weigh risks versus benefits before deciding whether long-term hydrocortisone therapy should be implemented.Conversely another retrospective cohort study found that although COVID-19 patients receiving methylprednisolone had better prognosis compared those without methylprednisolone treatment,longer methylprednisolone did not result worse outcome rather improved prognosis inconsistent with our findings therefore further research needed clarifying this discrepancy.Our study limitations include lack randomization selection bias residual confounders inherent single center retrospective cohort design therefore prospective randomized controlled trials needed confirming our findings.Detailed discussion regarding limitations please refer Supplementary Material section “Limitations”. 77 Conclusions Our study indicated although overall COVID-19 patients receiving glucocorticoids had better prognosis compared those without glucocorticoids treatment,longer glucocorticoids resulted worse outcome rather improved prognosis.Moreover multiple studies demonstrated benefits related short-term glucocorticoids especially critically ill COVID-19 population.Our study supported this conclusion.Conversely other studies suggested long-term glucocorticoid may improve even non-critically ill population.Hence our findings did not support this conclusion.Therefore our findings emphasized importance selecting appropriate patient population when considering long-term glucocorticoid.Compared other retrospective cohort studies consistent conclusions found whereas inconsistent conclusions found therefore further research needed clarifying discrepancies.Limitations inherent single center retrospective cohort design prospective randomized controlled trials needed confirming our findings.Detailed discussion regarding limitations please refer Supplementary Material section “Limitations”. 78 ## Supplementary Information 79 Additional file 1Supplementary Material Details concerning data extraction process Description concerning how each parameter listed Tableslabeled “Major medical history”,”Clinical parameters”,”Laboratory examination”,”Severity indicators”,”Outcome indicator” obtained Description concerning how each parameter listed Tables labeled “Demographic characteristics”, “Major medical history”, “Clinical parameters”, “Laboratory examination”, “Severity indicators”,