Epidemiological Investigation Of HAP Pathogens And Establishment Of The Predictive Scoring Model Of ABA-HAP

Part 1 Analysis of the distribution and drug susceptibility of 5155 strains of pathogenic bacteria from HAP patientsObjective: To analyze the epidemiological data of pathogenic bacteria from HAP patients in Tang Du Hospital in recent 3 years to provide reference for empirical treatment of HAP.Methods: The results of routine bacterial culture and drug susceptibility of respiratory tract specimens submitted for clinical examination by patients with HAP in our hospital during January 2017 to December 2019 were collected.Results: A total of 5155 qualified strains were collected,and Acinetobacter baumannii(ABA)accounted for the most(26.7%),followed by Pseudomonas aeruginosa(PAE)(19.6%),Klebsiella pneumoniae(KPN)(14.4%)and so on.ABA showed high sensitivity to polycolistin,tegacycline and cefoperazone-sulbactan(76.12%),while most antibiotics less than 20%.The detection rate of MDR-ABA,XDR-ABA,CR-ABA were78.49%,12.28% and 84.81%,respectively.The sensitivity of PAE to amikacin was high up to 86.26%,and that of imipenem and meropenem was lower,about 60%.The detection rate of MDR-PAE,XDR-PAE,CR-PAE were 34.42%,3.46% and 38.18%,respectively.The detection rate of CRE was 11.67%.and the detection rate of MRSA was 46.03%.Conclusion: The resistance of HAP pathogens to common antibiotics is very serious,especially ABA and PAE,and the high detection rate of MDR-ABA,XDR-ABA,CR-ABA,CR-PAE,CRE and MRSA needs clinical attention.Part 2 Derivation and validation of a predictive scoring model of infections due to Acinetobacter baumannii in patients with GNB-HAPObjective: To develop a predictive scoring model for hospital acquired Acinetobacter baumannii pneumonia(ABA-HAP)to guide the initial empirical antimicrobial treatment.Patients and Methods: A single center retrospective study was performed among patients with Hospital acquired GNB pneumonia(GNB-HAP)in our hospital during January 2019 to June 2019(the derivation cohort,DC).The variables were assessed on the day when qualified respiratory tract specimens were obtained.The primary outcome variable was ABA-HAP.A prediction score was formulated by using independent risk factors obtained from logistic regression analysis.It was validated with a subsequent cohort of GNB-HAP patients admitted to our hospital during July 2019 to Dec 2019(the validation cohort,VC).Model discrimination was determined by AUROC.Results: DC and VC included 395 and 362 patients,respectively.The final logistic regression model of DC included the following variables: Transferred from other hospitals(3 points);Blood purification(3 points);Risk for aspiration(4 points);Immunocompromised(3 points);Pulmonary interstitial pathological changes(3 points);Pleural effusion(1 points);Heart failure(3 points);Encephalitis(5 points);Increased monocyte count(2 points);and Increased neutrophils count(2 points).The AUROC of the scoring model for predicting ABA-HAP was 0.845(95% CI,0.796 ～ 0.895)in DC.When the cutoff value was 8 points,the sensitivity of the predictive model was more than 90%,which could reduce the rate of missed diagnosis.If the total score was < 8 points,it was more probable to diagnose non-ABA-HAP.When the cutoff value was 12 points,the specificity of the predictive model was more than 90%,which could reduce the rate of misdiagnosis.When the total score was ≥ 12 points,it was more probable to diagnose ABA-HAP.According to different scores,patients could be divided into ABA-HAP low-risk group(the score < 8 points),moderate-risk group(12 points > the score ≥ 8points)and high-risk group(the score ≥ 12 points).The AUROC of the scoring model was0.807(95% CI,0.759 ～ 0.856)in VC,and the incidence of ABA-HAP in the low-risk group,the moderate-risk group and the high-risk group was 4.8%(7/147),16.1%(29/180),66.2%(45/68)in DC and 4.5%(4/89),20.8%(31/149),53.2%(66/124)in VC,respectively,and the difference was statistically significant(P < 0.001).Conclusion: Our predictive scoring model has been verified to be of high value in predicting ABA-HAP and distinguishing low-risk and high-risk patients.This model will help clinicians to make decisions on initial empirical antibacterial treatment and implement specific interventions,thus increasing the probability of patients receiving early and appropriate empirical antibacterial treatment,decreasing the mortality of patients.