Prognostic evaluation of quick sequential organ failure assessment score in ICU patients with sepsis across different income settings
Andrew LiLowell LingHanyu QinYaseen M. ArabiSheila Nainan MyatraMoritoki EgiJe Hyeong KimMohd Basri Mat NorDo Ngoc SonWen‐Feng FangBambang WahyuprajitnoMadiha HashmiMohammad Omar FaruqBoonsong PatjanasoontornMaher Jaffer Al BahraniBabu Raja ShresthaUjma ShresthaKhalid Mahmood Khan NafeesKyi Kyi SannJose Emmanuel M. PaloNaranpurev MendsaikhanAidos KonkayevKhamsay DetleuxayYiong Huak ChanBin DuJigeeshu Vasishtha DivatiaYounsuck KohJason PhuaUzzal Kumar MallickMotiul IslamTarequl HamidA. K. M. Shirazul IslamRabiul HalimMd. Arifur Rahman KhanMohammad AsaduzzamanMd Rezaul KarimNahim SarwarShamsul Hoque MilonRashed MahmudA. K. M. Sirajul Islam HirokAshraful HaqueAmina SultanaMir Atiqur Rahman ShajalFarha AndalibRashedul HasanKhalid Mahmood Khan NafeesShah Sudhirchandra DhansukhlalNing LiXiaowei LiuHaiwei YangMing HouYing LiJian ZhangLifeng HuangWenxiong LiMeili DuanTaotao LiuWei HeFangyu NingXiaozhi WangXiaoyan ZhouSun YuXiang XiangPan LiangFeihu ZhouYaoli WangJian ZhouTao WangXuefei YangYu MaXuan SongHaiying WuChuanyun QianLixin ZhouZuohang XuKun ZhangZhenjie HuXingsheng LinSongjing ShiXiaoguang ZhangRongguo YuLiqin ZhangYuan YuanHuiru ZhouXiandong WangZhonghua WangTiehe QinXianqing ShiRui LiZhenyang HeXiangrong ZuoQuan CaoTao HeYuanda SuiTiejun WuYing XuQin GuWeizheng ShuaiHanyu QinBin DuHong QiaoShuangling LiGuiying DongXiujuan ZhaoFengxue ZhuJunshi WangLei HuangTianchang WangHao WangSiqing MaZhengping YangYuan GaoRuoming TanYun XieRuilan WangJia JiaBin ZangJun WangLing LinYuwen WuYunfu WuPenglin MaYanfang LiWen H. YuRui GuoJiuzhi ZhangXianyao WanFeng ShenQindong ShiJun XuQiang FangShaohua LiuTongwen SunMian ZengWeiyun PanZhongmin LiuQingling LinNan WangJing PangBin XiongDeliang WenFu-xin KangLiuhui ChangYun SunJingxiao ZhangYongjie YinQing LiuJiajun SunNahui LiYongqiang WangSongtao ShouYanfen ChaiLei XuXiaobo YangXuelian LiaoXian KangShuangping ZhaoLiquan HuangRun ZhangRenhua SunChao ShenYan HeFu Loi ChowMichele W. TangPhilip W. LamEsther ChamKin Bong TangLowell LingManimala DharmangadanPauline Yeung NgKin Ho LingVincent I. LauSamir SahuSharmila ChatterjeeSushmita BasuZubair Umer MohamedSudeep SirgaSiddhartha Reddy KasireddyM. A. AleemSwarna Deepak KuragayalaSai Praveen HaranathNagarajan RamakrishnanPravin AminJoanne MascarenhasRadhika DashVenkat Raman KolaR VaidyanathanSiddharth AgarwalPradip K. BhattacharyaDeepak JeswaniParshotum Lal GautamAbdul Samad AnsariVivek NangiaMrinal SircarVinoth BalasubramaniS. ManeendraSanghamitra MishraAnjeev KumarRajesh ChawlaTrevor Francis SequeiraO. P. ShrivastavaT. V. SreevalsanRajesh Mohan ShettyManjunath ThimmappaM. M. HarishYatin MehtaDivya SaxenaVipul Kumar MishraRishi KumarSimnt Kumar JhaPrashant SakhavalkarDnyaneshwar DiwaneSubhal DixitKalaiselvanManoranjan PattnaikLalit SinghFareed KhanMehul ShahPrasannaZiokov JoshiSheila Ninan MyatraManoj GoradeBharat G JagiasiAmol HartalkarB Saroj Kumar PrustyYogesh YogeshAde WinataMaulydiaSurya Oto WijayaHermin PrihartiniShinta V. R. HutajuluRudy ManaluChristrijogo SumartonoChrisma Adryana AlbandjarIra PitalokaDewi KusumawatiArifin ArifinAkhmad Yun JufanBambang Pujo SemediVanessy Theodora SilalahiYudiantoErwin PradianAchsanuddin HanafieMariza FitriatiTinni T. MaskoenSatriawan AbadiCalcarina Fitriani Retno WisudartiJohan ArifinReza Widyanto SudjudPrananda Surya AirlanggaRupi’iMade WiryanaAnang AchmadiPatra Rijalul HarlyEdward KusumaPrimartanto WibowoAde Veronica HYJeni Sarah MandangMeriwijantiI Wayan AryabiantaraFaisal MuchtarFachrul Jamal IsaDita AditianingsiihNicolaas Parningotan SimamoraMoch HasyimI. Gusti Putu ManuabaNovita AnggraeniRudy Ariyanto SanoesiArief MunandarDuma Saurma SiahaanSri RachmawatiOky SusiantoLiliriawati Ananta KaharZulkifli ZulkifliMordekhai L. LaihadTaka‐aki NakadaYoshitaka HaraOsamu NishidaKenji UeharaMakoto TakatoriShinichiro OhshimoKazuya KikutaniNobuaki ShimeShin NunomiyaShinshu KatayamaBengo AtariTakashi ItoYasuyuki KakihanaKohei TakimotoMachi YanaiMoritoki EgiTomoaki YatabeYuki KishiaraUshio HigashijimaMotohiro SekinoKazuaki AtagiHiroshi OguraTsunehiro MatsubaraTadashi KamioShigeki FujitaniToru YoshidaYukari AoyagiShigehiko UchinoMasatsugu HasegawaJun OtoNaoki YamaguchiYuki EnomotoMasaki NakaneG. S. AmirovaMurat DaribaevMarkov Viktor EvgenievichAnton A. VorobievAlice AndrushenkoAliya TorpakbaevaM. E. KonkayevaА. В. ГалкинP. A. OstaninKhamsay DetleuxayNoryani Mohd SamatIsmail TanNahla Irtiza IsmailChew Har LimWan Nasrudin Wan IsmailSiti Rohayah SulaimanAnita AliasJoanne Tiong Jia WenAzmin Huda Abdul RahimAsmah ZainudinNik Azman Nik AdibZihni AbdullahHafizahMohd Zulfakar MazlanMohd Basri Mat NorMunkhasiakhanNaranpurevCho Myint TunThinzar MawCho ChoHan SeinMyo Malar WinLwin Lwin HninCho Cho LwinAye Su MonYi Sandar TheinKhin Le Le YiMyo Min NaingNu Nu MayLun NaingKhin Saw Yu AungMoe Thu LinAung KyiKyaw Min TunSuu New KhinKhin Pyone YiKhin May WaanMoe ThidarKyi Kyi SannMu Mu NaingWin Win MarNaing Naing LinLalit Kumar RajbanshiTrishant LimbuBaburaja ShresthaUjma ShresthaAshish ShresthaRosi PradhanRavi Ram ShresthaSulav AcharyaPramesh Sunder ShresthaPuja Thapa KarkiMoosa AwladthaniJacob PaulNadia Al BadiAdil Al KharusiKhalil Al KharousiSandeep KantorYohannan JohnSaid Al MandhariGeetha JacobAmr Muhammad EsmatB. M. J. ShettyAhmed MostafaNaveed RashidMuhammad SohaibSonia JosephSafia ZafarAhmed FarooqMuhammad Sheharyar AshrafTanveer HussainMuhammad HayatAta-ur RehmanSyed Muneeb AliSaad ur RehmanAshok KumarAaron Mark HernandezCrystal AperochoRaymundo ResurreccionDebbie Noblezada-UyJose Emmanuel PaloJulie Christie VisperasAmer AsiriAli BeshabshiFahad Al-HameedOhoud Al OrabiYaseen M. ArabiEman Al QasimMasood IqbalTharwat AisaMohammed Saeed Al ShahraniLaila Perlas AsontoAyman KharabaAbdullah Al MutairiKhaild Al GhamdiLama HefniAhmad Al QurashiGaleb Al MakhlafiRoshni Sadashiv GokhaleNoelle LimManjit PawarKumaresh VenkatesanNaville Chia Chi HockTan Chee KeatTan Rou AnJared De SouzaAndrew LiYip Hwee SengJason PhuaAddy Tan YHMelvin Tay Chee KiangNg Shin YiHo Vui KianKiran SharmaSennen Jin Wen LewLee Rui MinDowan KimYoon Mi ShinSong-I. LeeKyung Chan KimYun-Seong KangSoo Hwan LeeHo Cheol KimYun Su SimSunghoon ParkTai Sun ParkHongyeul LeeYoujin ChangHeung Bum LeeJe Hyeong KimYoung Seok LeeWon Gun KwackIn Byung KimTae Yun ParkYoung‐Jae ChoSang‐Min LeeKyeongman JeonJong Min LeeShin Young KimJin Won HuhJong Joon AhnJae Hwa ChoWon‐Yeon LeeChin‐Kuo LinChang-Ke ChuJiun‐Ting WuChiung-Yu LinYu‐Mu ChenKuo‐Tung HuangHan‐Chung HuCong-Tat CiaJung‐Yien ChienChun-Te HuangPin‐Kuei FuNattachai SrisawasManasnun KongwibulwutKaweesak ChittawatanaratWorapot DaewtrakulchaiAnakapong PhunmaneeAnupol PanitchoteBoonsong PatjanasoontornChaiwut SawawiboonLê Minh TrungĐỗ Ngọc SơnByoung‐Chun HaDương Thiện PhướcHuỳnh Quang ĐạiNguyễn Tấn HùngLê Thị ThúyHoàng Bùi HảiHoàng Trọng Ái QuốcTrần Hoài LinhVũ Hải YếnPhạm Trà GiangNguyễn Thị Hằng NgaNguyễn Đăng Tuân
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Abstract Background There is conflicting evidence on association between quick sequential organ failure assessment (qSOFA) and sepsis mortality in ICU patients. The primary aim of this study was to determine the association between qSOFA and 28-day mortality in ICU patients admitted for sepsis. Association of qSOFA with early (3-day), medium (28-day), late (90-day) mortality was assessed in low and lower middle income (LLMIC), upper middle income (UMIC) and high income (HIC) countries/regions. Methods This was a secondary analysis of the MOSAICS II study, an international prospective observational study on sepsis epidemiology in Asian ICUs. Associations between qSOFA at ICU admission and mortality were separately assessed in LLMIC, UMIC and HIC countries/regions. Modified Poisson regression was used to determine the adjusted relative risk (RR) of qSOFA score on mortality at 28 days with adjustments for confounders identified in the MOSAICS II study. Results Among the MOSAICS II study cohort of 4980 patients, 4826 patients from 343 ICUs and 22 countries were included in this secondary analysis. Higher qSOFA was associated with increasing 28-day mortality, but this was only observed in LLMIC ( p < 0.001) and UMIC ( p < 0.001) and not HIC ( p = 0.220) countries/regions. Similarly, higher 90-day mortality was associated with increased qSOFA in LLMIC ( p < 0.001) and UMIC ( p < 0.001) only. In contrast, higher 3-day mortality with increasing qSOFA score was observed across all income countries/regions ( p < 0.001). Multivariate analysis showed that qSOFA remained associated with 28-day mortality (adjusted RR 1.09 (1.00–1.18), p = 0.038) even after adjustments for covariates including APACHE II, SOFA, income country/region and administration of antibiotics within 3 h. Conclusions qSOFA was independently associated with 28-day mortality in ICU patients admitted for sepsis. In LLMIC and UMIC countries/regions, qSOFA was associated with early to late mortality but only early mortality in HIC countries/regions. Graphical AbstractPractical and ethical constraints mean that many clinical and/or epidemiological questions cannot be answered through the implementation of a randomized controlled trial. Under these circumstances, observational studies are often required to assess relationships between certain exposures and disease outcomes. Unfortunately, observational studies are notoriously vulnerable to the effect of different types of “confounding,” a concept that is often a source of confusion among trainees, clinicians and users of health information. This article discusses the concept of confounding by way of examples and offers a simple guide for assessing the impact of is effects for learners of evidence-based medicine.
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We conducted a systematic literature search in Medline to assess the proportion of observational intervention studies appreciating confounding bias in peer-reviewed medical literature from 1985 through 2005. This study shows only 9% of all papers on observational intervention studies published in peer-reviewed medical journals mention any of the terms (confounding, adjustment, or bias) indicating appreciation of confounding.
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There are usually unknown or unmeasured confounders in the observational study, which is a significant challenge in epidemiological causal association research. This paper presents a tool for identification and effect assessment of unknown/unmeasured confounders in observational studies: probe variables. It can be divided into three forms: exposure probe variable, outcome probe variable, and mediation probe variable. The first two types can identify unknown/unmeasured confounding factors and estimate their size of effect to reveal the real correlation between exposure and outcome. The mediation probe variable controls for "mediating factors" to identify unmeasured confounders between exposure and results. The most significant difficulty in this theory's practice is selecting and determining "probe variables." Improper probe variables may introduce unknown confounders, which may lead to false identification of unmeasured confounders. Probe variables can be recommended as a sensitivity analysis in observational studies to help readers truly understand the association between exposure and outcomes and to increase the strength of evidence in observational epidemiological studies.观察性研究中往往存在未知或未测量的混杂因素，是流行病学因果关联研究中的重大挑战。本文介绍一种可以应用在观察性研究中的一种对未知/未测量混杂因素进行识别和效应评估的工具——“探针变量”。其主要可以分为暴露探针变量、结局探针变量以及中介探针3种形式，前2种不仅可以对未知/未测量混杂因素进行识别，也可以对其效应量进行估计，从而揭示真实的暴露与结局之间的关联。而中介探针则是针对“中介因子”进行控制，从而识别暴露和结局之间是否存在未测量混杂因素。该理论实践过程中最大的困难在于“探针变量”的选择和确定，不恰当的“探针变量”可能引入新的混杂，导致未测量混杂因素识别不准确。“探针变量”可以推荐作为观察性研究报告中的一项敏感性分析内容，有助于读者真实理解暴露与结局之间的关联，增加观察性流行病学研究中的证据力度。.
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We consider estimating the conditional average treatment effect for everyone by eliminating confounding and selection bias. Unfortunately, randomized clinical trials (RCTs) eliminate confounding but impose strict exclusion criteria that prevent sampling of the entire clinical population. Observational datasets are more inclusive but suffer from confounding. We therefore analyze RCT and observational data simultaneously in order to extract the strengths of each. Our solution builds upon Difference in Differences (DD), an algorithm that eliminates confounding from observational data by comparing outcomes before and after treatment administration. DD requires a parallel slopes assumption that may not apply in practice when confounding shifts across time. We instead propose Synthesized Difference in Differences (SDD) that infers the correct (possibly non-parallel) slopes by linearly adjusting a conditional version of DD using additional RCT data. The algorithm achieves state of the art performance across multiple synthetic and real datasets even when the RCT excludes the majority of patients.
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Observational epidemiological studies are increasingly used in pharmaceutical research to evaluate the safety and effectiveness of medicines. Such studies can complement findings from randomized clinical trials by involving larger and more generalizable patient populations by accruing greater durations of follow‐up and by representing what happens more typically in the clinical setting. However, the interpretation of exposure effects in observational studies is almost always complicated by non‐random exposure allocation, which can result in confounding and potentially lead to misleading conclusions. Confounding occurs when an extraneous factor, related to both the exposure and the outcome of interest, partly or entirely explains the relationship observed between the study exposure and the outcome. Although randomization can eliminate confounding by distributing all such extraneous factors equally across the levels of a given exposure, methods for dealing with confounding in observational studies include a careful choice of study design and the possible use of advanced analytical methods. The aim of this paper is to introduce some of the approaches that can be used to help minimize the impact of confounding in observational research to the reader working in the pharmaceutical industry. Copyright © 2011 John Wiley & Sons, Ltd.
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This Guide to Statistics and Methods discusses E-value analysis, an alternative approach to sensitivity analyses for unmeasured confounding in observational studies that specifies the degree of unmeasured confounding that would need to be operative to negate observed results in a study.
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Journal Article Methodologic Issues in Hospital Epidemiology. IV. Risk Ratios, Confounding, Effect Modification, and the Analysis of Multiple Variables Get access Jonathan Freeman, Jonathan Freeman Please address requests for reprints to Dr. Jonathan Freeman, Channing Laboratory, 180 Longwood Avenue, Boston, Massachusetts 02115. Search for other works by this author on: Oxford Academic PubMed Google Scholar Donald A. Goldmann, Donald A. Goldmann Search for other works by this author on: Oxford Academic PubMed Google Scholar John E. McGowan, Jr. John E. McGowan, Jr. Search for other works by this author on: Oxford Academic PubMed Google Scholar Reviews of Infectious Diseases, Volume 10, Issue 6, November 1988, Pages 1118–1141, https://doi.org/10.1093/clinids/10.6.1118 Published: 01 November 1988 Article history Received: 06 July 1987 Revision received: 17 March 1988 Published: 01 November 1988
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In this article, we presented the rationale and calculation procedures of the propcnsity score matching (PSM), and its application in the designing stage of an cpidcrniological study. Based on existing observational data, PSM can be used to select one or more comparable controls for each subject in 'treatment' group according to the propensity scores estimated by 'treatment' variable and main covariates. The results of an example analysis showed that the bias for main confounders between the treated and control samples declined more than 55% after PMS. Conclusion: PSM can reduce most of the confounding bias of the observational study, and can obtain approximate study effect to the randomized controlled trials when used in the designing of thc cpidcmiological study.
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Propensity score matching; Confounding bias; Epidemiology
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