SpO2/FiO2Ratio as a Better Metric for Assessment of RBC Transfusion Effectiveness in Non-traumatic Critically Ill Patients with Physiologic Derangements

SpO2/FiO2 Ratio as a Better Metric for Assessment of RBC Transfusion Effectiveness in Non-traumatic Critically Ill Patients with Physiologic Derangements | medRxiv /* */ /* */ <!-- <!-- /*! * yepnope1.5.4 * (c) WTFPL, GPLv2 */ (function(a,b,c){function d(a){return"[object Function]"==o.call(a)}function e(a){return"string"==typeof a}function f(){}function g(a){return!a||"loaded"==a||"complete"==a||"uninitialized"==a}function h(){var a=p.shift();q=1,a?a.t?m(function(){("c"==a.t?B.injectCss:B.injectJs)(a.s,0,a.a,a.x,a.e,1)},0):(a(),h()):q=0}function i(a,c,d,e,f,i,j){function k(b){if(!o&&g(l.readyState)&&(u.r=o=1,!q&&h(),l.onload=l.onreadystatechange=null,b)){"img"!=a&&m(function(){t.removeChild(l)},50);for(var d in y[c])y[c].hasOwnProperty(d)&&y[c][d].onload()}}var j=j||B.errorTimeout,l=b.createElement(a),o=0,r=0,u={t:d,s:c,e:f,a:i,x:j};1===y[c]&&(r=1,y[c]=[]),"object"==a?l.data=c:(l.src=c,l.type=a),l.width=l.height="0",l.onerror=l.onload=l.onreadystatechange=function(){k.call(this,r)},p.splice(e,0,u),"img"!=a&&(r||2===y[c]?(t.insertBefore(l,s?null:n),m(k,j)):y[c].push(l))}function j(a,b,c,d,f){return q=0,b=b||"j",e(a)?i("c"==b?v:u,a,b,this.i++,c,d,f):(p.splice(this.i++,0,a),1==p.length&&h()),this}function k(){var a=B;return a.loader={load:j,i:0},a}var l=b.documentElement,m=a.setTimeout,n=b.getElementsByTagName("script")[0],o={}.toString,p=[],q=0,r="MozAppearance"in l.style,s=r&&!!b.createRange().compareNode,t=s?l:n.parentNode,l=a.opera&&"[object Opera]"==o.call(a.opera),l=!!b.attachEvent&&!l,u=r?"object":l?"script":"img",v=l?"script":u,w=Array.isArray||function(a){return"[object Array]"==o.call(a)},x=[],y={},z={timeout:function(a,b){return b.length&&(a.timeout=b[0]),a}},A,B;B=function(a){function b(a){var a=a.split("!"),b=x.length,c=a.pop(),d=a.length,c={url:c,origUrl:c,prefixes:a},e,f,g;for(f=0;f<d;f++)g=a[f].split("="),(e=z[g.shift()])&&(c=e(c,g));for(f=0;f<b;f++)c=x[f](c);return c}function g(a,e,f,g,h){var i=b(a),j=i.autoCallback;i.url.split(".").pop().split("?").shift(),i.bypass||(e&&(e=d(e)?e:e[a]||e[g]||e[a.split("/").pop().split("?")[0]]),i.instead?i.instead(a,e,f,g,h):(y[i.url]?i.noexec=!0:y[i.url]=1,f.load(i.url,i.forceCSS||!i.forceJS&&"css"==i.url.split(".").pop().split("?").shift()?"c":c,i.noexec,i.attrs,i.timeout),(d(e)||d(j))&&f.load(function(){k(),e&&e(i.origUrl,h,g),j&&j(i.origUrl,h,g),y[i.url]=2})))}function h(a,b){function c(a,c){if(a){if(e(a))c||(j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}),g(a,j,b,0,h);else if(Object(a)===a)for(n in m=function(){var b=0,c;for(c in a)a.hasOwnProperty(c)&&b++;return b}(),a)a.hasOwnProperty(n)&&(!c&&!--m&&(d(j)?j=function(){var a=[].slice.call(arguments);k.apply(this,a),l()}:j[n]=function(a){return function(){var b=[].slice.call(arguments);a&&a.apply(this,b),l()}}(k[n])),g(a[n],j,b,n,h))}else!c&&l()}var h=!!a.test,i=a.load||a.both,j=a.callback||f,k=j,l=a.complete||f,m,n;c(h?a.yep:a.nope,!!i),i&&c(i)}var i,j,l=this.yepnope.loader;if(e(a))g(a,0,l,0);else if(w(a))for(i=0;i (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];var j=d.createElement(s);var dl=l!='dataLayer'?'&l='+l:'';j.src='//www.googletagmanager.com/gtm.js?id='+i+dl;j.type='text/javascript';j.async=true;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-P4HH5NV'); Skip to main content Home About Submit ALERTS / RSS Search for this keyword Advanced Search SpO 2 /FiO 2 Ratio as a Better Metric for Assessment of RBC Transfusion Effectiveness in Non-traumatic Critically Ill Patients with Physiologic Derangements Tilendra Choudhary , Geoffrey Smith , John D. Roback , Ravi M. Patel , Cassandra D. Josephson , Rishikesan Kamaleswaran doi: https://doi.org/10.1101/2024.10.03.24313829 Tilendra Choudhary 1 Duke University School of Medicine , Durham NC Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site For correspondence: tilendra.choudhary{at}duke.edu Geoffrey Smith 2 Emory University School of Medicine , Atlanta GA M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site John D. Roback 2 Emory University School of Medicine , Atlanta GA M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Ravi M. Patel 2 Emory University School of Medicine , Atlanta GA M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Cassandra D. Josephson 2 Emory University School of Medicine , Atlanta GA 3 Department of Oncology, The Johns Hopkins University School of Medicine , Baltimore, Maryland, USA 4 Cancer and Blood Disorders Institute, Johns Hopkins All Children’s Hospital , St. Petersburg, Florida, USA M.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Rishikesan Kamaleswaran 1 Duke University School of Medicine , Durham NC Ph.D. Find this author on Google Scholar Find this author on PubMed Search for this author on this site Abstract Full Text Info/History Metrics Supplementary material Data/Code Preview PDF ABSTRACT Identifying critically ill patients who are likely to improve their respiratory physiology following RBC blood transfusion is dynamic and difficult. Current decision tools are over-reliant on hemoglobin transfusion thresholds, without considering respiratory measures that may reflect physiologic effects of anemia and functional responses to RBC transfusion. Further, routine clinical measures to determine transfusion efficacy beyond hemoglobin increment are lacking to identify patients as responders or non-responders. We present a two-center retrospective cohort study aiming to determine a potential biomarker to assess the physiologic response of RBC transfusion for ICU patients. The study was performed with 13,274 eligible admissions at the first center. Another 3,757 from the second center were used for validation purpose. We introduced a comparative analysis of two respiratory measures, SpO 2 and SpO 2 /FiO 2 (SF) ratio, in addition to hemoglobin, to assess individual patient responses to RBC transfusion. A statistical study was performed to analyze the change in these markers before and after the transfusion interval. Based on quantitative statistical analyses, we found SF ratio to be a more effective biomarker than hemoglobin alone for revealing RBC transfusion efficacy. There existed an inverse correlation between pre-transfusion SF and transfusion efficacy. The results were consistent across both centers, revealing generalizability. Introduction Critical ill patients in intensive care units (ICU) often develop anemia and coagulation that are associated with adverse outcomes, such as high risk of life-threatening scenarios, thrombosis, and coronary artery diseases. To treat the anemia and coagulation following surgery, trauma or medical conditions, patients have been transfused with red blood cells (RBCs) for decades [ 1 , 2 ]. Annually about 85 million RBC units are used for transfusion worldwide, and approximately 15 million are transfused every year in the United States only. In transfusion medicine, a major physical injury or massive bleeding due to an accident, surgery, or uncontrolled intraoperative hemorrhage is typically referred as Trauma . Massive blood transfusions (MTs) are essential for traumatic patients. In contrast, reasons for non-traumatic blood transfusions include healthy blood cell deficiency, anemia, coagulation, and other health disorders (e.g., hemophilia, thrombocytopenia, kidney or liver disease, severe infection, and sickle cell disease). An assessment of transfusion response and transfusion necessity in non-traumatic ICU patients is more difficult than in traumatic patients. The hallmark trigger for initiating transfusions has been the criterion of hemoglobin (Hb) ≤7 g/dL [ 1 – 3 ]. However, a significant percentage of transfusions performed are often identified as inappropriate [ 4 , 5 ], thus contributing to adverse events such as acute immune hemolytic reactions and bloodborne infections [ 6 – 8 ]. There has been much interest in the discovery of alternative biomarkers that may indicate a quantitative utility of RBC transfusions. One potential non-invasive derived measure is the SpO 2 /FiO 2 (SF) ratio, which incorporates blood oxygen content along with oxygen concentration in gas exchange. This equation couples the intensity of respiratory support, the patient is receiving – FiO 2 , with the degree of hypoxemia – SpO 2 , the patient is experiencing. FiO 2 is a measure of inhaled atmospheric/supplemental oxygen content and can be titrated according to the SpO 2 measure. When oxygen supply and its consumption are incongruent, cells are damaged and subsequently die [ 9 ]. For instance, in chronic obstructive pulmonary disease (COPD), supplemental oxygen is recommended to start when the SpO 2 falls below 88% [ 9 ]. Clinically, SF315 normal [ 10 ]. Based on the potential for the SF ratio to provide a functional measure of oxygen requirements and delivery, the SF was hypothesized to be a more accurate biomarker for RBC transfusion response. This study endeavored to investigate the effect of RBC transfusion on SF ratio in a cohort of critically ill patients to determine the physiologic response to RBC transfusion. The study may help in devising decision-making tools, data benchmarking, and future research to optimize treatment strategies for RBC transfusion of ICU patients. Materials and Methods This study was reviewed and approved by the Emory University IRB (No. STUDY00000302) as non-human subjects, and waiver of informed consent was obtained. All procedures were followed in accordance with the ethical standards of the responsible committee on human experimentation (institutional/regional) and with the Helsinki Declaration of 1975. Continuous physiological data were archived using the BedMaster (Excel Medical, Jupiter, FL) software. SpO 2 was collected at 1-hour sampling interval and used to derive the SF ratio continuously from admission through to discharge for patients. Hemoglobin and FiO 2 values were derived from the electronic medical record for enrolled patients. The retrospective data was lastly accessed on Mar. 30 th , 2024, for this research. The authors had also access to information that could identify individual participants during or after data collection. In this article, we present a multi-center retrospective cohort study conducted at two high volume academic hospitals located in the southeastern U.S.A. Adult non-traumatic ICU patients (≥18 years) admitted to either of these two metropolitan hospitals, Emory University Hospital and Grady Memorial Hospital (Atlanta, GA) anytime in 2016-2020, who received RBC transfusion, were eligible for enrollment in the study. We adopted a multi-center derivation and validation study design by first establishing relation of SF and transfusion response using data from Emory University Hospital, and then validating this relation against data collected from Grady Memorial Hospital. Patients were excluded for the following reasons: a) if they had trauma and high bleeding injury who received massive transfusions, b) if they did not have continuous monitoring data archived during encounter, c) were discharged or died after ICU admission within 24 hours, due to limited duration of monitoring data available; and, d) who did not have continuous physiological data up to 24 hours before and after the transfusion. We also excluded successive RBC transfusion episodes occurred within next 24 hours of an RBC transfusion event to avoid data overlapping. We divided the timing of RBC transfusion into two windows, the period before the initiation of RBC transfusion and the period after its completion. Specifically, 24-hour data sequences were considered for the period before transfusion, and three subsequent post-RBC transfusion periods (T1, T2, T3; 9-hour duration each) were evaluated for the post-transfusion period, typically following a 4-hour period of RBC infusion. We applied median aggregation to the data of our cohort, across the pre-transfusion window and post-transfusion time-windows. An overview of our method is shown in Figure 1 . The reason for the selection of the three periods was to identify short-term vs. sustained improvements in respiratory physiology. Responses to RBC transfusions were characterized by SF differences (ΔSF) that were calculated between post- and pre-transfusion periods, considering four different ranges of baseline SF measurements (1-100, 101-200, 201-300, >300). An increase in the SF ratio indicated improvement in respiratory physiology (either from an SpO 2 improvement or an FiO 2 decrease). We further employed a polynomial curve fitting scheme to evaluate the relation between baseline SF and ΔSF, demonstrating which patient groups were more benefitted from transfusion. The following third-order polynomial function was used to model this relation in a least square sense. Download figure Open in new tab Figure 1: Overview of the study design. One pre-transfusion period of 24 hours and three post-transfusion periods of 9 hours each were used to analyze short-term vs. sustained physiological improvements. where a, b, c, d denote the modelling parameters. The association between ΔSF and difference in SpO 2 (ΔSpO 2 ) was also evaluated using the Bland Altman analysis. To evaluate the statistical significance ( p -value) among four baseline SF groups, Chi-squared and One-way ANOVA tests were used for categorical and numeric features, respectively. Whereas Pearson coefficient (r) and R-squared (R 2 ) values were used for evaluating correlation. Results We retrospectively evaluated a total of 82,767 patient admissions from 64,962 unique patients who were admitted to Emory ICUs from 2016-2020, of which 15,898 (∼20%) received at least one RBC transfusion with no massive transfusions. Among these, 14,765 had continuous physiologic bedside monitoring data within the temporal period close to the index RBC transfusion event. After discarding patients with missing physiological data, 13,274 were selected for the analysis and a total of 18,716 transfusion episodes were noted for which the baseline SF is not missing. Our cohort consists of transfused patients across a wide range of demographic features, such as age (mean: 61.4 ± 16 years), sex (male: 48.1%), and race (Caucasian: 46.6%, African American: 45%). Similarly, from the Grady Hospital, a total of 6,375 transfusion episodes from 3,757 patient encounters were included in the study. Clinical characteristics and demographics of transfusion patients from both the centers are listed in Tables 1 and 2 for their pre-transfusion observational data. View this table: View inline View popup Table 1: Clinical characteristics and demographic variables of the derivation dataset collected from Emory admitted RBC transfused patients based on their pre-transfusion aggregated features in four baseline SF groups – A: 1-100, B: 101-200, C: 201-300, D: >300. View this table: View inline View popup Download powerpoint Table 2: Clinical characteristics and demographic variables of the validation dataset collected from Grady admitted RBC transfused patients based on their pre-transfusion aggregated features in four baseline SF groups – A: 1-100, B: 101-200, C: 201-300, D: >300. For the Emory data as a derivation dataset, Table 1 lists distribution of transfused patients in four groups according to their baseline SF values as A: 1-100, B: 101-200, C: 201-300, D: >300. Initially, we attempted to establish a relation between post-transfusion SF variation and baseline Hb for four different baseline SF ranges with the derivation data. For the first post-transfusion segment T1, the relation between ΔSF and baseline Hb is presented for four different baseline SF groups in Figure 2 . We found that patients with the lowest baseline SF ratio (1-100), reflecting those most critically ill, consistently demonstrated an improvement in SF ratio subsequent to RBC transfusion with a mean increase of 75.9 (95% CI 71.4-80.4) after T1, 107.3 (95% CI 102.7-111.8) after T2, 123 (95% CI 118.4-127.6) after T3 (Supplemental e Figure 1 ). This improved effect was present across all three time periods (T1-T3), and independent of baseline Hb values (e.g. R 2 of -0.7, -1.4 and -2.0 for T1, T2 and T3, respectively, top row of Supplemental e Figure 1 ). For patients with a baseline SF >300, reflecting the population with the least respiratory compromise, no significant improvement in SF ratio was observed following RBC transfusion, and in fact respiratory physiology worsened after transfusion (an average decrease of 5.9 (95% CI 5.2-6.6) after T1, 7.3 (95% CI 6.6-8.1) after T2, and 8.4 (95% CI 6.5-8.1) after T3), with mean differences actually reflecting worsening of respiratory physiology. Again, differences in SF ratios were not associated with pre-transfusion Hb values (e.g. R 2 of -0.13, -0.14 and -0.15 for T1, T2 and T3, respectively, bottom row of Supplemental e Figure 1 ). Patients with more moderate baseline SF ratios had more heterogeneous responses to RBC transfusions with both positive and negative SF ratios (Supplemental e Figure 1 : 2nd and 3rd rows). Download figure Open in new tab Figure 2: Localized study on Emory hospital patients having different ranges of baseline SF ratios. ΔSF vs baseline Hb for first post-transfusion region T1 of duration 9 hours with baseline SF range (a) 1-100, (b) 101-200, (c) 201-300, and (d) >300. Note that data-points denote the distribution of patients. Red vertical line with blueish filled region around it represents the mean difference in SF with 95% confidence interval (CI). We also evaluated the relationship of baseline SF with RBC transfusion efficacy, expressed by ΔSF measures, using a polynomial curve fitting scheme. In this analysis, ΔSF is measured as difference in SF from pre-transfusion to first post-transfusion segment. The modelled resultant curve establishes an inverse relation of baseline SF and ΔSF, demonstrating a great transfusion benefit for patients having lower baseline SFs and the effect reduces for increasing baseline SF measure. It is shown in Figure 3 . Furthermore, an association of z-score normalized ΔSF and ΔSpO 2 was investigated using the Bland Altman and correlation analyses, which indicate a weak correlation (r=0.15, p <0.005) and poor agreement (95% limits of agreement LoA: [-2.6, 2.6]) for the derivation dataset. Their plots are illustrated in Figure 4 . Download figure Open in new tab Figure 3: Derivation data: Relation between baseline SF (BSF) and transfusion efficacy (ΔSF). Resultant curve modelled with a polynomial curve fitting demonstrates an inverse relation of BSF and ΔSF. Download figure Open in new tab Figure 4: Bland Altman analysis of normalized ΔSF and ΔSpO 2 on the derivation set. (a) correlation plot, and (b) Bland-Altman plot. To check the consistency and reproducibility of results, we validated our method with Grady Hospital data. Similar to the derivation data, Table 2 presents the distribution of transfused patients in four baseline SF groups for the validation data. For the first post-transfusion segment T1, the relation between ΔSF and baseline Hb is shown for all four baseline SF groups in Figure 5 ; whereas Supplemental e Figure 2 shows a detailed picture of this relationship for T1, T2 and T3. We observed that patients with the lowest baseline SF ratio (1-100) showed an improvement in SF ratio after RBC transfusion. While patients with a baseline SF >300 demonstrated no significant improvement in post-transfusion SF ratios. These findings demonstrated a similar pattern that was obtained by the derivation data, ensuring the consistency. Additionally, the resulting modelled curve on ΔSF and baseline SF, depicted in Figure 6 , showed an inverse relation of them, which was also consistent. Like for the derivation data, we also attempted to show the association of ΔSF with ΔSpO 2 using the Bland Altman analysis in Figure 7 , which also yielded poor and similar correlation (r=0.12, p <0.005) and agreement (LoA: [-2.61, 2.61]). Download figure Open in new tab Figure 5: Localized study on Grady hospital patients having different ranges of baseline SF ratios. ΔSF vs baseline Hb for first post-transfusion region T1 of duration 9 hours with baseline SF range (a) 1-100, (b) 101-200, (c) 201-300, and (d) >300. Note that data-points denote the distribution of patients. Red vertical line with blueish filled region around it represents the mean difference in SF with 95% confidence interval (CI). Download figure Open in new tab Figure 6: Validation data: Relation between baseline SF (BSF) and transfusion efficacy (ΔSF). Resultant curve modelled with a polynomial curve fitting demonstrates an inverse relation of BSF and ΔSF. Download figure Open in new tab Figure 7: Bland Altman analysis of normalized ΔSF and ΔSpO 2 on the validation data. (a) correlation plot, and (b) Bland-Altman plot. Discussion In the present study, we demonstrated how strongly baseline SF, i.e., pre-transfusion SF ratio is associated with the RBC transfusion response, shown by ΔSF measurement, with substantial experiments on two different centers. Critically ill patients with physiologic derangements having lowest SF 1-100 benefitted more from the RBC transfusion. While respiratory physiology of population with the least respiratory compromise (baseline SF >300) worsened subsequent to RBC transfusion. Observed consistency in results across both centers, this initial study establishes SF as a potential marker to evaluate the RBC transfusion efficacy. Figures 2 , 5 and e Figures 1 , 2 show almost linear and continuous decrement in post-transfusion SF change of patient-groups having incremental baseline SFs, and this effect is consistent throughout each of all three post-transfusion regions. Decrement in ΔSF is associated with the deterioration of transfusion efficacy. This effect was also shown via polynomial curve modeling approach. Also, the ΔSpO2 had poor agreement with ΔSF, highlighting that SpO2 alone may not reflect the changes in physiology informed by the more complete SF ratio. The present study is first of its kind, specifically designed for critically ill patients in ICU. The conventional clinical practice has been over reliant on Hb [ 1 – 3 ], where an increase in post-transfusion Hb is oblivious in anemic patients. Additionally, from Tables 1 and 2 , we observed a decreasing effect in baseline Hb and hematocrit ranges as we move from A to D population. Where patients in group D with highest baseline SF are least critical, characterized by smallest Sequential Organ Failure Assessment (SOFA) max total score, and patients in A with lowest baseline SF are most critical, reflecting large SOFA scores. Thus, an inability to uncover the impact on respiratory physiology subsequent to RBC transfusion makes Hb incapable to show the transfusion efficacy for ICU patients. To strengthen Hb-reliant conventional approach, our study introduced SF as a surrogate and showed that effectiveness of the RBC transfusion is strongly associated with the pre-transfusion SF ratio. However, future prospective studies and clinical trials may be required to validate the hypothesis for its more generalizability. Based on this initial study, we demonstrate that pre-transfusion SF ratio is significantly associated with RBC transfusion response, introducing SF ratio as a measure for RBC transfusion efficacy. Additionally, we show that SF ratio is not meaningfully correlated to SpO 2 or Hb. Our results suggest that additional studies evaluating the SF ratio as a biomarker to determine the response to RBC transfusion (e.g. ΔSF) and the neediest RBC transfusion recipients (e.g. SF<100) are warranted. Our findings are consistent with studies showing the lack of improvement in oxygenation following RBC for many critically ill patients and emphasize the need for pragmatic, readily available measures to more precisely identify those critically ill patients who are most likely to benefit from RBC transfusion. Data Availability All data produced in the present study are available upon reasonable request to the authors. Financial disclosures and conflicts of interest R Kamaleswaran and T Choudhary were supported by the National Institutes of Health under Award Numbers R01GM139967, R21GM151703, and R21GM14893. The authors declare no competing financial interests. Authors contributions R.K. conceived of the study and developed the study design. T.C. performed data and research analysis and wrote the manuscript; R.M.P. and R.K. helped with interpretation, editing of the manuscript, and read and approved the final manuscript; C.D.J., G.S., and J.D.R. read, suggested feedbacks, and approved the final manuscript. Acknowledgements The authors would like to thank the anonymous reviewers in advance for their useful suggestions and comments. R Kamaleswaran and T Choudhary were supported by the National Institutes of Health under Award Numbers R01GM139967, R21GM151703, and R21GM14893. Footnotes E-mail addresses: geoffrey.smith{at}emory.edu (Geoffrey Smith), jroback{at}emory.edu (John D. Roback), rmpatel{at}emory.edu (Ravi M. Patel), cjoseph{at}emory.edu (Cassandra D. Josephson), r.kamaleswaran{at}duke.edu (Rishikesan Kamaleswaran) References 1. ↵ Carson JL , Stanworth SJ , Guyatt G , Valentine S , Dennis J , Bakhtary S , et al. Red Blood Cell Transfusion: 2023 AABB International Guidelines . JAMA . 2023 ; 330 : 1892 – 1902 . doi: 10.1001/jama.2023.12914 OpenUrl CrossRef 2. ↵ Carson JL , Guyatt G , Heddle NM , Grossman BJ , Cohn CS , Fung MK , et al. Clinical Practice Guidelines From the AABB: Red Blood Cell Transfusion Thresholds and Storage . JAMA . 2016 ; 316 : 2025 – 2035 . doi: 10.1001/jama.2016.9185 OpenUrl CrossRef PubMed 3. ↵ Cable CA , Razavi SA , Roback JD , Murphy DJ . RBC Transfusion Strategies in the ICU: A Concise Review . Crit Care Med . 2019 ; 47 : 1637 – 1644 . doi: 10.1097/CCM.0000000000003985 OpenUrl CrossRef 4. ↵ Goodnough LT , Verbrugge D , Vizmeg K , Riddell J. Identifying elective orthopedic surgical patients transfused with amounts of blood in excess of need: the transfusion trigger revisited . Transfusion (Paris) . 1992 ; 32 : 648 – 653 . doi: 10.1046/j.1537-2995.1992.32792391039.x OpenUrl CrossRef PubMed Web of Science 5. ↵ Shander A , Fink A , Javidroozi M , Erhard J , Farmer SL , Corwin H , et al. Appropriateness of allogeneic red blood cell transfusion: the international consensus conference on transfusion outcomes . Transfus Med Rev . 2011 ; 25 : 232 - 246.e53 . doi: 10.1016/j.tmrv.2011.02.001 OpenUrl CrossRef PubMed 6. ↵ Rohde JM , Dimcheff DE , Blumberg N , Saint S , Langa KM , Kuhn L , et al. Health care-associated infection after red blood cell transfusion: a systematic review and meta-analysis . JAMA . 2014 ; 311 : 1317 – 1326 . doi: 10.1001/jama.2014.2726 OpenUrl CrossRef PubMed Web of Science 7. Vamvakas EC . Establishing causation in transfusion medicine and related tribulations . Transfus Med Rev . 2011 ; 25 : 81 – 88 . doi: 10.1016/j.tmrv.2010.11.010 OpenUrl CrossRef PubMed 8. ↵ Sonmezoglu M , Aydinok Y , Canatan D , Turker M , Gulen H , Irken G , et al. Transfusion Transmitted Viral Infections in Transfusion Dependent Patients with Thalassemia in Turkey: A Multicenter Study . Blood . 2004 ; 104 : 3782 . doi: 10.1182/blood.V104.11.3782.3782 OpenUrl Abstract / FREE Full Text 9. ↵ Fuentes S , Chowdhury YS . Fraction of Inspired Oxygen . StatPearls . Treasure Island (FL ): StatPearls Publishing ; 2022 . Available: http://www.ncbi.nlm.nih.gov/books/NBK560867/ 10. ↵ Shimizu M , Hashimoto S. Peripheral oxygen saturation to inspiratory oxygen fraction ratio-based identification of critically ill coronavirus disease patients for early therapeutic interventions . J Anesth . 2021 ; 35 : 827 – 836 . doi: 10.1007/s00540-021-02986-w OpenUrl CrossRef View the discussion thread. Back to top Previous Next Posted October 04, 2024. Download PDF Supplementary Material Data/Code Email Thank you for your interest in spreading the word about medRxiv. NOTE: Your email address is requested solely to identify you as the sender of this article. Your Email * Your Name * Send To * Enter multiple addresses on separate lines or separate them with commas. You are going to email the following SpO2/FiO2 Ratio as a Better Metric for Assessment of RBC Transfusion Effectiveness in Non-traumatic Critically Ill Patients with Physiologic Derangements Message Subject (Your Name) has forwarded a page to you from medRxiv Message Body (Your Name) thought you would like to see this page from the medRxiv website. Your Personal Message CAPTCHA This question is for testing whether or not you are a human visitor and to prevent automated spam submissions. Share SpO 2 /FiO 2 Ratio as a Better Metric for Assessment of RBC Transfusion Effectiveness in Non-traumatic Critically Ill Patients with Physiologic Derangements Tilendra Choudhary , Geoffrey Smith , John D. Roback , Ravi M. Patel , Cassandra D. Josephson , Rishikesan Kamaleswaran medRxiv 2024.10.03.24313829; doi: https://doi.org/10.1101/2024.10.03.24313829 Share This Article: Copy Citation Tools SpO 2 /FiO 2 Ratio as a Better Metric for Assessment of RBC Transfusion Effectiveness in Non-traumatic Critically Ill Patients with Physiologic Derangements Tilendra Choudhary , Geoffrey Smith , John D. Roback , Ravi M. Patel , Cassandra D. Josephson , Rishikesan Kamaleswaran medRxiv 2024.10.03.24313829; doi: https://doi.org/10.1101/2024.10.03.24313829 Citation Manager Formats BibTeX Bookends EasyBib EndNote (tagged) EndNote 8 (xml) Medlars Mendeley Papers RefWorks Tagged Ref Manager RIS Zotero Tweet Widget Facebook Like Google Plus One Subject Area Health Informatics Subject Areas All Articles Addiction Medicine (573) Allergy and Immunology (865) Anesthesia (304) Cardiovascular Medicine (4457) Dentistry and Oral Medicine (445) Dermatology (383) Emergency Medicine (610) Endocrinology (including Diabetes Mellitus and Metabolic Disease) (1517) Epidemiology (15244) Forensic Medicine (30) Gastroenterology (1132) Genetic and Genomic Medicine (6620) Geriatric Medicine (669) Health Economics (1002) Health Informatics (4557) Health Policy (1372) Health Systems and Quality Improvement (1615) Hematology (543) HIV/AIDS (1272) Infectious Diseases (except HIV/AIDS) (15936) Intensive Care and Critical Care Medicine (1106) Medical Education (624) Medical Ethics (147) Nephrology (670) Neurology (6634) Nursing (346) Nutrition (999) Obstetrics and Gynecology (1148) Occupational and Environmental Health (957) Oncology (3348) Ophthalmology (980) Orthopedics (369) Otolaryngology (421) Pain Medicine (436) Palliative Medicine (130) Pathology (665) Pediatrics (1696) Pharmacology and Therapeutics (693) Primary Care Research (714) Psychiatry and Clinical Psychology (5463) Public and Global Health (9257) Radiology and Imaging (2210) Rehabilitation Medicine and Physical Therapy (1371) Respiratory Medicine (1198) Rheumatology (598) Sexual and Reproductive Health (716) Sports Medicine (532) Surgery (714) Toxicology (99) Transplantation (289) Urology (265) (function(){function c(){var b=a.contentDocument||a.contentWindow.document;if(b){var d=b.createElement('script');d.innerHTML="window.__CF$cv$params={r:'a03666e1bf031b23',t:'MTc4MDA2NzE3NA=='};var a=document.createElement('script');a.src='/cdn-cgi/challenge-platform/scripts/jsd/main.js';document.getElementsByTagName('head')[0].appendChild(a);";b.getElementsByTagName('head')[0].appendChild(d)}}if(document.body){var a=document.createElement('iframe');a.height=1;a.width=1;a.style.position='absolute';a.style.top=0;a.style.left=0;a.style.border='none';a.style.visibility='hidden';document.body.appendChild(a);if('loading'!==document.readyState)c();else if(window.addEventListener)document.addEventListener('DOMContentLoaded',c);else{var e=document.onreadystatechange||function(){};document.onreadystatechange=function(b){e(b);'loading'!==document.readyState&&(document.onreadystatechange=e,c())}}}})();