[摘要]"及時的膿毒癥患者預(yù)后判斷、精準(zhǔn)識別高風(fēng)險群體是降低膿毒癥死亡率的關(guān)鍵策略。隨著對膿毒癥研究的深入,風(fēng)險預(yù)測評分系統(tǒng)、生物標(biāo)志物和風(fēng)險預(yù)測模型逐步應(yīng)用于膿毒癥的預(yù)后評估中,以提高臨床醫(yī)生對膿毒癥患者預(yù)后判斷的精確度。本文綜述當(dāng)前膿毒癥患者預(yù)后評估的最新研究進展,以期為膿毒癥的臨床管理與科研探索提供參考。
[關(guān)鍵詞]"膿毒癥;預(yù)后評估;生物標(biāo)志物;評分系統(tǒng);預(yù)測模型
[中圖分類號]"R459.7""""""[文獻標(biāo)識碼]"A""""""[DOI]"10.3969/j.issn.1673-9701.2024.35.028
“sepsis”源自希臘語,意為分解、腐爛。20世紀(jì)初,其含義逐漸演變成現(xiàn)代醫(yī)學(xué)中的“膿毒癥”。1991年,膿毒癥1.0(sepsis"1.0)被定義為由感染引起的全身炎癥反應(yīng)綜合征。而相較于既往版本,研究團隊在膿毒癥3.0(sepsis"3.0)中納入序貫器官衰竭評估(sequential"organ"failure"assessment,SOFA)評分,以識別并評估膿毒癥[1]。
1""膿毒癥概述
Rudd等[2]將低中等收入國家的流行病學(xué)數(shù)據(jù)納入研究范圍,結(jié)果顯示1990—2017年,年齡標(biāo)準(zhǔn)化膿毒癥的發(fā)病率和死亡率均較之前下降,且膿毒癥的發(fā)病率和死亡率存在顯著地區(qū)差異。2017—2019年,中國膿毒癥年標(biāo)準(zhǔn)化發(fā)病率從328.25/10萬增長至421.85/10萬,膿毒癥院內(nèi)死亡率達28.4%,且膿毒癥的院內(nèi)死亡率呈逐年上升趨勢[3]。目前,膿毒癥的發(fā)病機制尚未完全明確。膿毒癥的病理生理過程涵蓋全身炎癥反應(yīng)、組織器官損傷、凝血功能異常、免疫功能障礙、微生物毒素作用等機制,還涉及神經(jīng)-內(nèi)分泌-免疫網(wǎng)絡(luò)失衡、細胞自噬、基因多態(tài)性等方面,與機體多器官、多系統(tǒng)密切相關(guān)[4-5]。
2""風(fēng)險預(yù)測評分系統(tǒng)
2.1""SOFA評分
SOFA評分由Vincent等[6]提出,用于評估呼吸、凝血、肝臟、心血管、腎臟、中樞神經(jīng)系統(tǒng)功能障礙/衰竭。2016年,sepsis"3.0納入SOFA評分。然而,SOFA評分依賴大量實驗室數(shù)據(jù),時效性較低,限制了其在急診科中的應(yīng)用。
2.2""快速SOFA評分
為增強可行性和實用性,專家學(xué)者提出更簡易的評分系統(tǒng),即快速SOFA(quick"SOFA,qSOFA)評分。該評分僅納入“意識形態(tài)改變、收縮壓≤100mmHg(1mmHg=0.133kPa)、呼吸頻率≥22次/min”3個指標(biāo)。盡管qSOFA評分在設(shè)計上力求簡便快捷,但在臨床應(yīng)用中其有效性和準(zhǔn)確性受到一定程度的爭議。
為提高qSOFA評分預(yù)測膿毒癥患者預(yù)后風(fēng)險的敏感度,專家學(xué)者對其進行不斷改良,形成降鈣素原增強qSOFA(procalcitonin-enhanced"qSOFA,PqSOFA)評分、改良qSOFA(modified"qSOFA,MqSOFA)評分等。PqSOFA評分將降鈣素原(procalcitonin,PCT)引入qSOFA評分。Xia等[7]在821例成人急性膿毒癥患者中進行回顧性分析,將患者PCTgt;0.5ng/ml計為1分,證實PqSOFA評分顯著優(yōu)于qSOFA評分。MqSOFA評分是將氧合指數(shù)引入qSOFA,外周血氧飽和度(peripheral"oxygen"saturation,SpO2)/吸入氧濃度(inspired"fraction"of"oxygen,F(xiàn)iO2)≥315評分計為0分,SpO2/FiO2在314~236范圍內(nèi)評分計為1分,SpO2/FiO2≤235評分計為2分。Guarino等[8]研究發(fā)現(xiàn)MqSOFA評分預(yù)測不同病因所致膿毒癥患者28d死亡率顯著優(yōu)于qSOFA評分。
2.3""急性生理學(xué)和慢性健康狀況評價Ⅱ評分
急性生理學(xué)和慢性健康狀況評價Ⅱ(acute"physiology"and"chronic"health"evaluation"Ⅱ,APACHE"Ⅱ)評分是臨床上重要的評估膿毒癥病情及預(yù)后的工具[9]。研究證實APACHE"Ⅱ評分是膿毒癥患者死亡的獨立危險因素,評分越高預(yù)示患者的預(yù)后越差、病死率越高[10-11]。在臨床實踐中應(yīng)結(jié)合患者的實際情況,綜合評估APACHE"Ⅱ評分的參考價值。
2.4""急診科膿毒血癥死亡率評分
急診科膿毒血癥死亡率(mortality"in"emergency"department"sepsis,MEDS)評分根據(jù)急診科特點設(shè)計,涵蓋終末期疾病、呼吸困難、感染性休克、血小板計數(shù)、桿狀核中性粒細胞、年齡、下呼吸道感染、居住在療養(yǎng)院及意識改變共9項指標(biāo)[12]。MEDS評分范圍為0~27分,根據(jù)評分將患者分為5個死亡風(fēng)險亞組,包括極低危、低危、中危、高危、極高危。MEDS評分在預(yù)測全身炎癥反應(yīng)綜合征、膿毒癥急診患者28d死亡風(fēng)險方面表現(xiàn)卓越,還可預(yù)測急診感染患者1年遠期病死率。國際上已有多項研究證實MEDS評分對評估急診科膿毒癥患者1個月死亡風(fēng)險的準(zhǔn)確性極高,甚至優(yōu)于SOFA評分和其他評分系統(tǒng)[13-15]。MEDS評分的部分評價指標(biāo)依賴臨床醫(yī)生的主觀判斷。盡管現(xiàn)有研究已廣泛證實該評分預(yù)測院內(nèi)死亡風(fēng)險的準(zhǔn)確性,但針對各死亡風(fēng)險亞組病死率的詳細驗證仍顯不足[16]。
3""預(yù)后生物標(biāo)志物
近年來,除使用相關(guān)量表評估膿毒癥患者預(yù)后外,生物標(biāo)志物也用于膿毒癥患者的預(yù)后判斷[17]。
3.1""血常規(guī)相關(guān)標(biāo)志物
血小板計數(shù)在炎癥反應(yīng)中的重要性逐漸被探究[18]。研究顯示膿毒癥休克患者在病程初始24h內(nèi)出現(xiàn)血小板減少,這與28d死亡率顯著升高緊密相關(guān)[19]。紅細胞體積分布寬度(red"cell"volume"distribution"width,RDW)作為衡量紅細胞體積異質(zhì)性的指標(biāo),其重要性在膿毒癥預(yù)后評估中逐漸顯現(xiàn)。伍羿等[20]通過回顧性研究發(fā)現(xiàn),RDW水平可作為膿毒癥患者死亡的獨立預(yù)測因子,表現(xiàn)為RDW水平每上升1%,患者的死亡率增加18%。Huang等[21]研究發(fā)現(xiàn)膿毒癥死亡患者的RDW水平在入院首周內(nèi)持續(xù)攀升。單核細胞分布寬度(monocyte"distribution"width,MDW)是反映血液中單核細胞分布情況的指標(biāo)。膿毒癥發(fā)生時,不僅單核細胞的數(shù)量有所變化,其大小、功能、表型等也呈現(xiàn)多樣化;且隨著疾病嚴(yán)重程度的加劇更加明顯,直觀表現(xiàn)為單核細胞形態(tài)的改變[22]。Malinovska等[23]研究揭示,MDW水平的升高與病情嚴(yán)重程度及器官障礙的發(fā)生呈正相關(guān)。
3.2""炎癥標(biāo)志物
C反應(yīng)蛋白(C-reactive"protein,CRP)是經(jīng)典的非特異性炎癥標(biāo)志物,在肝臟內(nèi)合成。在機體發(fā)生嚴(yán)重感染、膿毒癥或嚴(yán)重組織損傷時,CRP水平可急劇升至正常水平的千倍以上,并促進多種炎癥介質(zhì)的釋放,加速器官組織損傷[24]。Koozi等[25]納入851例重癥監(jiān)護病房(intensive"care"unit,ICU)膿毒癥患者的研究發(fā)現(xiàn),CRPgt;100mg/L患者的死亡風(fēng)險顯著增加。
PCT是降鈣素的前體物質(zhì),在生理狀態(tài)下主要由甲狀腺濾泡旁細胞產(chǎn)生。引入PCT的PqSOFA評分可用于膿毒癥患者的預(yù)后評估,其單獨使用對預(yù)測膿毒癥患者的預(yù)后也有極高價值。除生理功能外,PCT的基因表達受脂多糖和全身炎癥介質(zhì)的誘導(dǎo)。Samuel等[26]研究發(fā)現(xiàn)PCT可很好地預(yù)測膿毒癥患者的預(yù)后,且PCTgt;2ng/ml較PCTgt;0.5ng/ml的特異性更佳,更具預(yù)測價值。Liang等[27]通過回顧性分析146例ICU膿毒癥患者證實,PCT與膿毒癥患者預(yù)后相關(guān),且PCT是患者28d死亡率的獨立危險因素。
血清淀粉樣蛋白A(serum"amyloid"A,SAA)是一種急性時相反應(yīng)蛋白,其通過多種信號通路參與炎癥反應(yīng)[28]。譚睿等[29]研究闡明,SAA水平及其動態(tài)變化在預(yù)測膿毒癥患者預(yù)后方面有重要作用,其中24hnbsp;AA水平預(yù)測的效果最佳。
3.3""白細胞介素
白細胞介素(interleukin,IL)-10是一種主要由輔助性T細胞2、脂多糖等刺激單核巨噬細胞和上皮細胞產(chǎn)生的抑制因子,可調(diào)節(jié)過量促炎性細胞因子和趨化因子所致的免疫宿主反應(yīng)[30]。既往研究表明,IL-10的過表達可作為預(yù)測膿毒癥患者死亡的顯著標(biāo)志[31]。IL-37屬于IL-1家族,主要由造血細胞產(chǎn)生,是固有免疫反應(yīng)的天然抑制劑,是調(diào)節(jié)炎癥反應(yīng)的重要細胞因子[32]。林芳崇等[33]研究表明,IL-37在膿毒癥患者中呈高水平表達,且其水平隨病情進展而逐漸升高,更是影響患者28d預(yù)后的獨立危險因素。
3.4""可溶性CD14亞型sCD14-ST
可溶性CD14亞型sCD14-ST主要分布于血清中;在全身炎癥反應(yīng)期間,sCD14-ST由血漿蛋白酶切割可溶性CD14后形成[34]。sCD14-ST對機體炎癥反應(yīng)、感染性休克、器官衰竭的發(fā)生發(fā)展具有重要意義[35]。Liu等[36]研究指出,sCD14-ST是評估膿毒癥患者預(yù)后的可靠指標(biāo)。研究證實膿毒癥死亡組患者的sCD14-ST水平顯著高于存活組,且病情嚴(yán)重患者的sCD14-ST水平更高[35]。
4""風(fēng)險預(yù)測模型
近年來,隨著大數(shù)據(jù)與人工智能(artificial"intelligence,AI)的蓬勃發(fā)展,機器學(xué)習(xí)、列線圖模型等先進工具被廣泛應(yīng)用于臨床研究中,促進眾多風(fēng)險預(yù)測模型的誕生與發(fā)展,這些模型涵蓋疾病的早期預(yù)測與診斷及預(yù)后預(yù)測等方面。研究表明基于大數(shù)據(jù)和機器學(xué)習(xí)的篩查工具可提高膿毒癥診斷的敏感度和準(zhǔn)確性[37]。篩查模型通過AI技術(shù)對臨床數(shù)據(jù)進行持續(xù)監(jiān)測,可提前數(shù)小時預(yù)測膿毒癥的發(fā)生,準(zhǔn)確率接近90%,較傳統(tǒng)疾病嚴(yán)重程度評分有很大提高[38]。Nemati等[39]通過構(gòu)建人工智能膿毒癥專家(artificial"intelligence"sepsis"expert,AISE)模型預(yù)測膿毒癥,結(jié)果顯示AISE模型可提前4~12h預(yù)測膿毒癥的發(fā)生。Mao等[40]將基于機器學(xué)習(xí)的膿毒癥預(yù)測算法InSight模型應(yīng)用于ICU患者膿毒癥發(fā)生的預(yù)測中,結(jié)果顯示其可提前4h預(yù)測膿毒癥的發(fā)生。既往研究主要集中于ICU危重癥膿毒癥患者中,近期Meta分析結(jié)果顯示,與應(yīng)用于ICU患者相比,AI預(yù)警系統(tǒng)在急診科和普通病房中的益處更明顯[41]。Honeyford等[42]將基于圣約翰膿毒癥算法開發(fā)的數(shù)字膿毒癥警報用于急診膿毒癥患者的早期預(yù)測,結(jié)果顯示其可早期預(yù)測膿毒癥的發(fā)生,改善患者預(yù)后,降低病死率。Burdick等[43]應(yīng)用機器學(xué)習(xí)算法預(yù)測膿毒癥,結(jié)果顯示其可有效預(yù)測嚴(yán)重膿毒癥的發(fā)生,改善患者預(yù)后。Gupta等[44]應(yīng)用樹形增強型樸素貝葉斯網(wǎng)絡(luò)開發(fā)的膿毒癥預(yù)測模型的受試者操作特征曲線下面積(area"under"the"curve,AUC)為0.840。García-Gallo等[45]采用隨機梯度推進法建立的膿毒癥預(yù)測模型的AUC為0.804。綜上,AI預(yù)測模型對膿毒癥預(yù)后的預(yù)測較臨床醫(yī)生的判斷及傳統(tǒng)的評分系統(tǒng)更精準(zhǔn),具有顯著優(yōu)勢。然而,上述模型中的大部分研究均為回顧性研究,各項研究之間存在較大的異質(zhì)性,無法相互驗證,尚不具備廣泛適用性。
5""小結(jié)與展望
膿毒癥的預(yù)后與多種因素相關(guān),早期識別高風(fēng)險患者可為臨床提供及時干預(yù)依據(jù),從而降低患者病死率。目前臨床上用于評估膿毒癥預(yù)后的工具眾多,包括風(fēng)險預(yù)測評分系統(tǒng)、生物標(biāo)志物和風(fēng)險預(yù)測模型等。然而,各種工具均有其優(yōu)勢及局限性,尚未形成統(tǒng)一、公認(rèn)的膿毒癥預(yù)后預(yù)測“金標(biāo)準(zhǔn)”。鑒于膿毒癥居高不下的發(fā)病率和死亡率,其依然是全球亟待解決的熱點問題之一。隨著AI、機器算法在醫(yī)學(xué)領(lǐng)域的普及和廣泛應(yīng)用,利用風(fēng)險預(yù)測模型評估膿毒癥患者的預(yù)后將成為未來發(fā)展的主流趨勢,但需考慮膿毒癥在不同人種、地域中的異質(zhì)性,以便構(gòu)建更有針對性的預(yù)測模型,確保其在不同臨床環(huán)境中的實用性和準(zhǔn)確性。
利益沖突:所有作者均聲明不存在利益沖突。
[參考文獻]
[1] RHODES"A,"EVANS"L"E,"ALHAZZANI"W,"et"al."Surviving"sepsis"campaign:"International"guidelines"for"management"of"sepsis"and"septic"shock:"2016[J]."Crit"Care"Med,"2017,"45(3):"486–552.
[2] RUDD"K"E,"JOHNSON"S"C,"AGESA"K"M,"et"al."Global,"regional,"and"national"sepsis"incidence"and"mortality,"1990–2017:"Analysis"for"the"global"burden"of"disease"study[J]."Lancet,"2020,"395(10219):"200–211.
[3] WENG"L,"XU"Y,"YIN"P,"et"al."National"incidence"and"mortality"of"hospitalized"sepsis"in"China[J]."Crit"Care,"2023,"27(1):"84.
[4] 姚詠明,"張艷敏."膿毒癥發(fā)病機制最新認(rèn)識[J]."醫(yī)學(xué)研究生學(xué)報,"2017,"30(7):"678–683.
[5] 欒櫻譯,"祝筱梅,"姚詠明."關(guān)于膿毒癥的發(fā)生機制與識別和干預(yù)[J]."中華危重病急救醫(yī)學(xué),"2021,"33(5):"513–516.
[6] VINCENT"J"L,"MORENO"R,"TAKALA"J,"et"al."The"SOFA"(sepsis-related"organ"failure"assessment)"score"to"describe"organ"dysfunction/failure."On"behalf"of"the"working"group"on"sepsis-related"problems"of"the"European"society"of"intensive"care"medicine[J]."Intensive"Care"Med,"1996,"22(7):"707–710.
[7] XIA"Y,"ZOU"L,"LI"D,"et"al."The"ability"of"an"improved"qSOFA"score"to"predict"acute"sepsis"severity"and"prognosis"among"adult"patients[J]."Medicine"(Baltimore),"2020,"99(5):"e18942.
[8] GUARINO"M,"PERNA"B,"DE"GIORGI"A,"et"al."A"2-year"retrospective"analysis"of"the"prognostic"value"of"MqSOFA"compared"to"lactate,"NEWS"and"qSOFA"in"patients"with"sepsis[J]."Infection,"2022,"50(4):"941–948.
[9] HAI"P"D,"VIET"HOA"L"T."The"Prognostic"accuracy"evaluation"of"mNUTRIC,"APACHE"Ⅱ,"SOFA,"and"SAPS"2"scores"for"mortality"prediction"in"patients"with"sepsis[J]."Crit"Care"Res"Pract,"2022,"2022:"4666594.
[10] 李艷秀,"左祥榮,"曹權(quán)."PCT聯(lián)合APACHEⅡ評分對ICU肺部感染合并膿毒癥的評估[J]."南京醫(yī)科大學(xué)學(xué)報(自然科學(xué)版),"2018,"38(12):"1725–1728.
[11] KUMAR"S,"GATTANI"S"C,"BAHETI"A"H,"et"al."Comparison"of"the"performance"of"APACHE"Ⅱ,"SOFA,"and"mNUTRIC"scoring"systems"in"critically"ill"patients:"A"2-year"cross-sectional"study[J]."Indian"J"Crit"Care"Med,"2020,"24(11):"1057–1061.
[12] SHAPIRO"N"I,"WOLFE"R"E,"MOORE"R"B,"et"al."Mortality"in"emergency"department"sepsis"(MEDS)"score:"A"prospectively"derived"and"validated"clinical"prediction"rule[J]."Crit"Care"Med,"2003,"31(3):"670–675.
[13] VAFAEI"A,"HEYDARI"K,"HASHEMI-NAZARI"S"S,"et"al."PIRO,"SOFA"and"MEDS"scores"in"predicting"one-month"mortality"of"sepsis"patients:"A"diagnostic"accuracy"study[J]."Arch"Acad"Emerg"Med,"2019,"7(1):"e59.
[14] ELBAIH"A"H,"ELSAYED"Z"M,"AHMED"R"M,"et"al."Sepsis"patient"evaluation"emergency"department"(SPEED)"score"amp;"mortality"in"emergency"department"sepsis"(MEDS)"score"in"predicting"28-day"mortality"of"emergency"sepsis"patients[J]."Chin"J"Traumatol,"2019,"22(6):"316–322.
[15] PONG"J"Z,"KOH"Z"X,"SAMSUDIN"M"I,"et"al."Validation"of"the"mortality"in"emergency"department"sepsis"(MEDS)"score"in"a"Singaporean"cohort[J]."Medicine"(Baltimore),"2019,"98(34):"e16962.
[16] 張愷,"張淑芳,"張根生."膿毒癥評分體系的研究進展[J]."中華急診醫(yī)學(xué)雜志,"2021,"30(10):"1279–1282.
[17] PIERRAKOS"C,"VELISSARIS"D,"BISDORFF"M,"et"al."Biomarkers"of"sepsis:"Time"for"a"reappraisal[J]."Crit"Care,"2020,"24(1):"287.
[18] SHEN"Y,"HUANG"X,"ZHANG"W."Platelet-to-lymphocyte"ratio"as"a"prognostic"predictor"of"mortality"for"sepsis:"Interaction"effect"with"disease"severity-A"retrospective"study[J]."BMJ"Open,"2019,"9(1):"e022896.
[19] THIERY-ANTIER"N,"BINQUET"C,"VINAULT"S,"et"al."Is"thrombocytopenia"an"early"prognostic"marker"in"septic"shock?[J]."Crit"Care"Med,"2016,"44(4):"764–772.
[20] 伍羿,"王德宇,"趙祥庚."紅細胞分布寬度與老年膿毒癥患者死亡率的相關(guān)性[J]."中國老年學(xué)雜志,"2019,"39(17):"4274–4277.
[21] HUANG"R,"XIA"J,"WANG"J,"et"al."Red"blood"cell"distribution"width:"A"potential"prognostic"index"for"short-term"mortality"of"patients"admitted"to"emergency"department"for"acute"decompensation"of"liver"cirrhosis"?"[J]."Eur"J"Gastroenterol"Hepatol,"2018,"30(3):"328.
[22] 于佳琪,"梁群,"劉雨默,"等."單核細胞分布寬度在膿毒癥早期診斷和預(yù)后評估中的研究進展[J]."中國醫(yī)藥導(dǎo)報,"2023,"20(12):"40–43,"47.
[23] MALINOVSKA"A,"HINSON"J"S,"BADAKI-MAKUN"O,"et"al."Monocyte"distribution"width"as"part"of"a"broad"pragmatic"sepsis"screen"in"the"emergency"department[J]."J"Am"Coll"Emerg"Physicians"Open,"2022,"3(2):"e12679.
[24] 柴韓飛,"謝海波,"杜曉紅,"等."PCT、CRP和HBP對膿毒癥的診斷及預(yù)后評估價值[J]."現(xiàn)代實用醫(yī)學(xué),"2021,"33(5):"617–619.
[25] KOOZI"H,"LENGQUIST"M,"FRIGYESI"A."C-reactive"protein"as"a"prognostic"factor"in"intensive"care"admissions"for"sepsis:"A"Swedish"multicenter"study[J]."J"Crit"Care,"2020,"56:"73–79.
[26] SAMUEL"M"S,"LATHA"R,"KAVITHA"K,"et"al."A"study"on"biomarkers"of"sepsis"and"potential"role"of"procalcitonin"and"ferritin"marker"in"diagnosis,"prognosis"and"treatment[J]."J"Family"Med"Prim"Care,"2022,"11(6):"2608–2612.
[27] LIANGnbsp;P,"YU"F."Value"of"CRP,"PCT,"and"NLR"in"prediction"of"severity"and"prognosis"of"patients"with"bloodstream"infections"and"sepsis[J]."Front"Surg,"2022,"9:"857218.
[28] GABAY"C,"KUSHNER"I."Acute-phase"proteins"and"other"systemic"responses"to"inflammation[J]."N"Engl"J"Med,"1999,"340(6):"448–454.
[29] 譚睿,"楊鵬磊,"王晶,"等."載脂蛋白A-I聯(lián)合血清淀粉樣蛋白A判斷膿毒癥及膿毒癥休克患者的病情及預(yù)后價值[J]."中華急診醫(yī)學(xué)雜志,"2024,"33(5):"643–650.
[30] 陳琛,"蘇華."白細胞介素-10與嚴(yán)重膿毒癥患者疾病預(yù)后的關(guān)系[J]."武漢大學(xué)學(xué)報(醫(yī)學(xué)版),"2018,"39(3):"485–488,"492.
[31] TAMAYO"E,"FERNáNDEZ"A,"ALMANSA"R,"et"al."Pro-"and"anti-inflammatory"responses"are"regulated"simultaneously"from"the"first"moments"of"septic"shock[J]."Eur"Cytokine"Netw,"2011,"22(2):"82–87.
[32] 樊柳汝,"李金蘭,"李筱妍."膿毒癥生物標(biāo)志物研究進展[J]."中國急救醫(yī)學(xué),"2022,"42(7):"620–624.
[33] 林芳崇,"符超,"李芬."白細胞介素37表達水平與膿毒癥患者免疫功能及臨床預(yù)后關(guān)系[J]."創(chuàng)傷與急危重病醫(yī)學(xué),"2023,"11(6):"376–381.
[34] 曾茁,"彭毅志,"袁志強."膿毒癥生物標(biāo)志物的研究進展[J]."中華燒傷與創(chuàng)面修復(fù)雜志,"2023,"39(7):"679–684.
[35] 陸駿灝."比較sCD14-ST和PCT、CRP對膿毒癥的診斷和預(yù)后評估有效性[J]."系統(tǒng)醫(yī)學(xué),"2018,"3(24):"57–59.
[36] LIU"B,"CHEN"Y"X,"YIN"Q,"et"al."Diagnostic"value"and"prognostic"evaluation"of"presepsin"for"sepsis"in"an"emergency"department[J]."Crit"Care,"2013,"17(5):"R244.
[37] DELAHANTY"R"J,"ALVAREZ"J,"FLYNN"L"M,"et"al."Development"and"evaluation"of"a"machine"learning"model"for"the"early"identification"of"patients"at"risk"for"sepsis[J]."Ann"Emerg"Med,"2019,"73(4):"334–344.
[38] ISLAM"M"M,"NASRIN"T,"WALTHER"B"A,"et"al."Prediction"of"sepsis"patients"using"machine"learning"approach:"A"Meta-analysis[J]."Comput"Methods"Programs"Biomed,"2019,"170:"1–9.
[39] NEMATI"S,"HOLDER"A,"RAZMI"F,"et"al."An"interpretable"machine"learning"model"for"accurate"prediction"of"sepsis"in"the"ICU[J]."Crit"Care"Med,"2018,"46(4):"547 553.
[40] MAO"Q,"JAY"M,"HOFFMAN"J"L,"et"al."Multicentre"validation"of"a"sepsis"prediction"algorithm"using"only"vital"sign"data"in"the"emergency"department,"general"ward"and"ICU[J]."BMJ"Open,"2018,"8(1):"e017833.
[41] YANG"J,"HAO"S,"HUANG"J,"et"al."The"application"of"artificial"intelligence"in"the"management"of"sepsis[J]."Med"Rev,"2023,"3(5):"369–380.
[42] HONEYFORD"K,"COOKE"G"S,"KINDERLERER"A,"et"al."Evaluating"a"digital"sepsis"alert"in"a"London"multisite"hospital"network:"A"natural"experiment"using"electronic"health"record"data[J]."J"Am"Med"Inform"Assoc,"2020,"27(2):"274–283.
[43] BURDICK"H,"PINO"E,"GABEL-COMEAU"D,"et"al."Effect"of"a"sepsis"prediction"algorithm"on"patient"mortality,"length"of"stay"and"readmission:"A"prospective"multicentre"clinical"outcomes"evaluation"of"real-world"patient"data"from"US"hospitals[J]."BMJ"Health"Care"Inform,"2020,"27(1):"e100109.
[44] GUPTA"A,"LIU"T,"SHEPHERD"S."Clinical"decision"support"system"to"assess"the"risk"of"sepsis"using"tree"augmented"Bayesian"networks"and"electronic"medical"record"data[J]."Health"Informatics"J,"2020,"26(2):"841–861.
[45] GARCíA-GALLO"J"E,"FONSECA-RUIZ"N"J,nbsp;CELI"L"A,"et"al."A"machine"learning-based"model"for"1-year"mortality"prediction"in"patients"admitted"to"an"intensive"care"unit"with"a"diagnosis"of"sepsis[J]."Med"Intensiva"(Engl"Ed),"2020,"44(3):"160 170.
(收稿日期:2024–08–08)
(修回日期:2024–10–09)