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Erschienen in: BMC Infectious Diseases 1/2023

Open Access 01.12.2023 | Research

Prognostic models for estimating severity of disease and predicting 30-day mortality of Hypervirulent Klebsiella pneumoniae infections: a bicentric retrospective study

verfasst von: Jieen Huang, Yanzhu Chen, Ming Li, Shujin Xie, Huasheng Tong, Zhusheng Guo, Yi Chen

Erschienen in: BMC Infectious Diseases | Ausgabe 1/2023

Abstract

Background

Hypervirulent Klebsiella pneumoniae (hvKP) is emerging globally and can cause various, severe infections in healthy individuals. However, the clinical manifestations of hvKP infections are nonspecific, and there is no gold standard for differentiating hvKP strains. Our objective was to develop prognostic models for estimating severity of disease and predicting 30-day all-cause mortality in patients with hvKP infections.

Methods

We enrolled 116 patients diagnosed with hvKP infections and obtained their demographic and clinical data. Taking septic shock and acute respiratory distress syndrome (ARDS) as the primary outcomes for disease severity and 30-day all-cause mortality as the primary outcome for clinical prognosis, we explored the influencing factors and constructed prognostic models.

Results

The results showed that increased Acute Physiologic and Chronic Health Evaluation (APACHE) II score [odds ratio (OR) = 1.146; 95% confidence interval (CI), 1.059–1.240], decreased albumin (ALB) level (OR = 0.867; 95% CI, 0.758–0.990), diabetes (OR = 9.591; 95% CI, 1.766–52.075) and high procalcitonin (PCT) level (OR = 1.051; 95%CI, 1.005–1.099) were independent risk factors for septic shock. And increased APACHE II score (OR = 1.254; 95% CI, 1.110–1.147), community-acquired pneumonia (CAP) (OR = 11.880; 95% CI, 2.524–55.923), and extrahepatic lesion involved (OR = 14.718; 95% CI, 1.005–215.502) were independent risk factors for ARDS. Prognostic models were constructed for disease severity with these independent risk factors, and the models were significantly correlated with continuous renal replacement therapy (CRRT) duration, vasopressor duration, mechanical ventilator duration and length of ICU stay. The 30-day all-cause mortality rate in our study was 28.4%. Younger age [hazard ratio (HR) = 0.947; 95% CI, 0.923–0.973)], increased APACHE II score (HR = 1.157; 95% CI, 1.110–1.207), and decreased ALB level (HR = 0.924; 95% CI, 0.869–0.983) were the independent risk factors for 30-day all-cause mortality. A prediction model for 30-day mortality was constructed, which had a good validation effect.

Conclusions

We developed validated models containing routine clinical parameters for estimating disease severity and predicting 30-day mortality in patients with hvKP infections and confirmed their calibration. The models may assist clinicians in assessing disease severity and estimating the 30-day mortality early.
Hinweise
Jieen Huang, Yanzhu Chen and Ming Li contributed equally to this work.

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Abkürzungen
hvKP
Hypervirulent Klebsiella pneumoniae
cKP
Classic Klebsiella pneumoniae
ARDS
Acute respiratory distress syndrome
APACHE II
Acute Physiology and Chronic Health Evaluation II
CAP
Community-acquired pneumonia
CKD
Chronic kidney disease
WBC
White blood cell count
NEUT#
Neutrophil count
LYMPH#
Lymphocyte count
MONO#
Monocyte count
HGB
Hemoglobin
PLT
Platelet
CRP
C-reactive protein
PCT
Procalcitonin
ALT
Alanine aminotransferase
AST
Aspartate aminotransferase
TBIL
Total bilirubin
DBIL
Direct bilirubin
ALB
Albumin
GLU
Glucose
CREA
Creatinine
PT
Plasma prothrombin time
APTT
Activated partial thromboplastin time
FIB
Fibrinogen
PO2
Partial pressure of oxygen
PCO2
Partial pressure of carbon dioxide
FIO2
Fraction of inspired oxygen
OI
Oxygenation index
PEEP
Positive end expiratory pressure
LAC
Lactate
CRRT
Continuous renal replacement therapy
ICU
Intensive care unit
SD
Standard deviation
IQR
Interquartile range
ROC
Receiver operating characteristic
K-M curve
Kaplan–Meier curve
AUC
Area under (the) curve
OR
Odds ratio
CI
Confidence interval
HR
Hazard ratio
BSIs
Bloodstream infections
GNB
Gram-negative bacilli

Introduction

Hypervirulent Klebsiella pneumoniae (hvKP) has become a global pathogen in recent years [1]. hvKP, an invasive type of Klebsiella pneumoniae (KP), is characterized by severely invasive community-acquired infections in young and immunocompetent individuals with rare sites of infections, rapid progression, severe disease and poor prognosis [15].
Currently, the hvKP strain is differentiated from classic Klebsiella pneumoniae (cKP) based on some phenotypic, genotypic properties and determining factors [6]. Li G et al. reported that Galleria mellonella killing assay in conjugation with the string test could be used to accurately assess KP virulence and differentiate hvKP from cKP strains [7]. Russo TA et al. noted that peg-344, iroB, iucA, prmpA, prmpA2, and siderophore production greater than 30 μg/ml could accurately identify hvKP strains [1, 8, 9]. However, there is no universal standard for identifying all hvKP strains [10]. Furthermore, detections of genotype and the determining factors are not widely available, especially in developing countries, making it difficult to recognize hvKP infections early.
Clinical manifestations of hvKP infections, lacking specificity, vary upon the organ involved. Clinically, some patients with hvKP infections soon develop to to septic shock, acute respiratory distress syndrome (ARDS), multiorgan failure and death at final. Early identification of hvKP infections and prediction of disease severity and outcomes are crucial to improve the survival of hvKP-infected patients. Previous studies showed that risk factors for mortality included gastrointestinal fistula, increased Acute Physiology and Chronic Health Evaluation (APACHE) II score and Pitt bacteraemia score, metastatic infection, septic shock, acute respiratory failure and gas formation on imaging [1012]. To date, there is few report on risk factors for disease severity. Most of the existing studies of hvKP mainly focus on virulence factors or drug resistance factors at the genetic level, and little attention has been paid to clinical aspects, especially the disease assessment and prognosis models. Therefore, we concentrated on clinical aspects, retrospectively analyzed the demographic and clinical data of hvKP-infected patients to determine the risk factors and tried to construct the prognostic models for disease severity and prognosis.

Materials and methods

Study setting and design

Patients with hvKP infections firstly diagnosed at Binhaiwan Central Hospital of Dongguan and Dongguan Tungwah Hospital from September 2017 to September 2022, meeting the inclusion and exclusion criteria, were enrolled in this retrospective study. Demographic and clinical data were collected by two individuals. The protocol for this study was approved by the Medical Ethics Committee of Binhaiwan Central Hospital of Dongguan (No. 2021014).

Inclusion and exclusion criteria

The inclusion criteria were as follows:1. KP-infected patients with string test positive. 2. KP strains with one or more of genotype (rmpA, rmpA2, iucA, iroB, magA and peg344) positive [8, 13, 14]. 3. patients with complete clinical data.
The exclusion criteria were as follows: 1. Patients younger than 18 years old. 2. Patients giving up an active rescue. 3. Immunocompromised patients with history of malignancy (under treatment or in remission for less than five years), immunosuppressive disorders (congenital/acquired immunocompromise), use of immunosuppressive regimens (corticosteroid therapy 1 mg/kg/day prednisone equivalent or corticosteroid therapy for longer than one month, use of another immunosuppressant drug in a high dosage or for longer than one month) [1517].

Detection of virulence-associated features and genes

Hypermucoviscosity was identified by the positive string test. A positive string test was defined as the formation of a viscous string > 5 mm in length when bacterial colonies on an agar plate were stretched with an inoculation loop [3]. All KP isolates were stored at − 80 °C until they were sent to relevant institutions (Guangzhou Huayin Health Medical Group Co., Ltd.) for detection of virulence-associated features through targeted next-generation sequencing. The genotypic analysis was investigated by polymerase chain reaction with previously described primers [13]. High-throughput sequencing was performed using the Illumina MiSeq Reagent Nano Kit. The reads that were correctly aligned at both ends were compared with the reference gene sequence of each virulence gene in the virulence gene data, and finally, the number of reads for each virulence gene in each sample was obtained.

Variables collection and definition

Clinical features including age, gender, history of smoking or alcohol consumption, community acquired pneumonia (CAP), comorbidities [diabetes mellitus, hepatopathy, chronic kidney disease (CKD), cardiovascular disease], septic shock, ARDS, and APACHE II score were collected. Laboratory data within 24 h after admission were as follows: white blood cell count (WBC), neutrophil count (NEUT#), lymphocyte count (LYMPH#), monocyte count (MONO#), hemoglobin (HGB), platelet (PLT), C-reactive protein (CRP), procalcitonin (PCT), alanine aminotransferase (ALT), aspartate aminotransferase (AST), total bilirubin (TBIL), direct bilirubin (DBIL), albumin (ALB), glucose (GLU), creatinine (CREA), coagulation plasma prothrombin time (PT), activated partial thromboplastin time (APTT), fibrinogen (FIB), partial pressure of oxygen (PO2), partial pressure of carbon dioxide (PCO2), fraction of inspired oxygen (FIO2), oxygenation index (OI), positive end expiratory pressure (PEEP) and lactate (LAC). Data on lesions and antimicrobial regimens were as follows: infection lesion, number of lesions, number of pathogens, hepatic abscess, pulmonary abscess, bacteremia, initial antimicrobial regimens [piperacillin/third generation of cephalosporins (ceftazidime, ceftriaxone, cefixime), piperacillin/third generation of cephalosporins combined with beta-lactamase inhibitor (cefoperazone-sulbactam, piperacillin-tazobactam, cefotaxime-sulbactam), carbapenemes (meropenem, imipenem, biapenem), quinolones (levofloxacin, moxifloxacin), aminodycosides (amikacin), second generation of cephalosporins (cefamandole, cefuroxime)], and number of antimicrobials. Clinical outcomes: continuous renal replacement therapy (CRRT) duration, vasopressors duration, mechanical ventilator duration, length of intensive care unit (ICU) stay, length of hospital stay and 30-day survival status. Indices of CRRT, vasopressors, mechanical ventilator and ICU were defined as CRRT duration/length of hospital stay, vasopressors duration/length of hospital stay, mechanical ventilator duration/length of hospital stay, and length of ICU stay/length of hospital stay, respectively.
The primary outcomes for the severity of disease were septic shock and ARDS, while the primary outcome for the clinical prognosis was 30-day all-cause mortality.

Statistical analysis

SPSS software (version 13.0) was used for data analysis. Normally and nonnormally distributed continuous variables were summarized as the mean ± standard deviation (SD) and the median with interquartile range (IQR), respectively. Continuous variables were compared using Student’s t test or the Mann–Whitney U test, and categorical variables were analyzed by using the χ2 test or Fisher’s exact test. P < 0.05 was considered statistically significant.
Univariate and multivariate logistic and Cox regression analyses were used to evaluate the risk factors. Variables with P < 0.05 in the univariate analysis were analyzed in the multivariate model using the likelihood-ratio test. R software (version 4.2.1, CRAN) was used for the nomogram, validation calibration curve, forest plot, scatterplot, receiver operating characteristic (ROC) curve and Kaplan–Meier (K-M) curve.

Results

Clinical features of the patients

A total of 116 patients were enrolled in our study (Fig. 1), their average age was 55.94 ± 15.93 years and 83 (71.6%) patients were male (Table 1). No significant differences were observed in age, gender, history of smoking/alcohol consumption, CAP, or comorbidities (diabetes mellitus, hepatopathy, CKD, cardiovascular disease) between the two hospitals.
Table 1
Clinical variables associated with septic shock and ARDS of hvKP infections
Characteristics
Total n = 116
Non-septic shock n = 69(59.5%)
septic shock n = 47(40.5%)
P value of septic shock
Non-ARDS n = 37(31.9%)
ARDS n = 79(68.1%)
P value of ARDS
(Mean ± SD) or Median (IQR) or n (%)
(Mean ± SD) or Median (IQR) or n (%)
Age (y)
55.94 ± 15.93
55.09 ± 15.49
57.19 ± 16.64
0.487
59.70 ± 16.12
54.18 ± 15.63
0.082
Gender
   
0.495
  
0.125
 Female
33(28.4)
18(26.1)
15(31.9)
 
14(37.8)
19(24.1)
 
 Male
83(71.6)
51(73.9)
32(68.1)
 
23(62.2)
60(75.9)
 
Smoking
   
0.635
  
0.218
 No
89(76.7)
54(78.3)
35(74.5)
 
31(83.8)
58(73.4)
 
 Yes
27(23.3)
15(21.7)
12(25.5)
 
6(16.2)
21(26.6)
 
Alcohol consumption
   
0.342
  
0.210
 No
96(82.8)
59(85.5)
37(78.7)
 
33(89.2)
63(79.7)
 
 Yes
20(17.2)
10(14.5)
10(21.3)
 
4(10.8)
16(20.3)
 
CAP
   
0.003
  
0.000
 No
35(30.2)
28(40.6)
7(14.9)
 
23(62.2)
12(15.2)
 
 Yes
81(69.8)
41(59.4)
40(85.1)
 
14(37.8)
67(84.8)
 
Diabetes
   
0.000
  
0.089
 No
75(64.7)
56(81.2)
19(40.4)
 
28(75.7)
47(59.5)
 
 Yes
41(35.3)
13(18.8)
28(59.6)
 
9(24.3)
32(40.5)
 
Hepatopathy
   
0.682
  
0.441
 No
79(68.1)
48(69.6)
31(66.0)
 
27(73.0)
52(65.8)
 
 Yes
37(31.9)
21(30.4)
16(34.0)
 
10(27.0)
27(34.2)
 
CKD
   
1.000
  
0.968
 No
108(93.1)
64(92.8)
44(93.6)
 
35(94.6)
73(92.4)
 
 Yes
8(6.9)
5(7.2)
3(6.4)
 
2(5.4)
6(7.6)
 
Cardiovascular disease
   
0.278
  
0.037
 No
62(53.4)
34(49.3)
28(59.6)
 
25(67.6)
37(46.8)
 
 Yes
54(46.6)
35(50.7)
19(40.4)
 
12(32.4)
42(53.2)
 
APACHE II score
20.04 ± 12.51
14.87 ± 8.83
27.64 ± 13.30
0.000
9.46 ± 5.18
25.00 ± 11.84
0.000
WBC (109/L)
14.88(9.58,20.98)
14.83(11.26,17.63)
15.98(7.33,24.74)
0.752
14.93(10.50,16.63)
14.70(9.29,22.14)
0.613
NEUT# (109/L)
12.45(7.87,17.22)
12.25(8.95,14.52)
13.49(6.20,21.16)
0.402
12.51(8.85,14.23)
12.43(6.28,19.63)
0.768
LYMPH# (109/L)
1.03(0.55,1.77)
1.15(0.68,1.82)
0.92(0.42,1.76)
0.049
1.12(0.82,1.67)
1.01(0.46,1.81)
0.371
MONO# (109/L)
0.67(0.29,0.99)
0.76(0.42,1.13)
0.37(0.15,0.94)
0.019
0.76(0.67,1.18)
0.45(0.25,0.92)
0.045
HGB (g/L)
127.50(112.00,141.00)
131.00(115.00,142.00)
122.00(101.00,137.00)
0.065
122.00(114.00,136.00)
128.00(111.00,144.00)
0.340
PLT (109/L)
207.50(122.00,303.25)
216.00(175.00,306.00)
155.00(66.00,264.00)
0.005
201.00(158.00,311.00)
211.00(101.00,300.50)
0.414
CRP (mg/L)
101.62(6.14,184.64)
18.45(1.99,141.09)
141.68(54.23,200.00)
0.001
116.59(24.16,200.00)
64.70(5.00,181.15)
0.434
PCT (ng/mL)
3.33(0.22,30.83)
0.35(0.07,6.60)
19.07(2.95,42.01)
0.000
2.99(0.25,32.57)
3.33(0.21,31.70)
0.828
ALT (U/L)
44.75(19.38,75.08)
37.95(18.45,81.25)
47.90(21.25,74.00)
0.482
49.00(23.00,84.00)
42.00(19.00,74.00)
0.393
AST (U/L)
38.0(23.5,89.75)
38.00(22.00,75.00)
46.00(28.95,102.50)
0.098
40.00(26.00,75.00)
38.00(22.85,98.50)
0.879
TBIL (μmol/L)
13.40(9.48,21.98)
11.11(9.00,18.20)
16.00(10.70,27.40)
0.022
14.30(9.50,24.80)
12.10(9.40,19.80)
0.460
DBIL (μmol/L)
5.89(3.70,10.64)
5.03(2.91,6.89)
8.00(5.09,11.90)
0.001
6.40(4.00,12.70)
5.86(3.35,10.00)
0.339
ALB (g/L)
34.35 ± 6.86
37.32 ± 6.13
30.09 ± 5.50
0.000
37.18 ± 5.40
33.05 ± 7.10
0.004
GLU (mmol/L)
9.42(6.92,15.90)
8.47(6.80,10.50)
12.54(7.17,18.61)
0.006
7.13(6.23,9.72)
10.81(7.64,17.59)
0.001
CREA (μmoI/L)
86.50(66.45,135.64)
74.10(63.35,101.25)
110.100(84.40,176.20)
0.004
82.25(65.62,98.63)
90.10(66.70,157.11)
0.185
PT (s)
12.40(11.40,14.10)
11.65(10.90,12.68)
13.70(11.90,15.70)
0.000
11.70(10.80,13.00)
12.55(11.53,15.03)
0.006
APTT (s)
30.70(24.30,42.30)
27.10(22.65,31.87)
37.40(29.40,47.00)
0.000
27.80(22.50,32.00)
32.45(25.83,45.80)
0.006
FIB (g/L)
4.13(2.80,6.13)
3.72(2.42,6.23)
5.00(3.41,6.13)
0.235
5.00(3.45,6.44)
4.06(2.54,6.01)
0.151
Abscess
   
0.180
  
0.002
 No
82(70.7)
52(75.4)
30(63.8)
 
19(51.4)
63(79.7)
 
 Yes
34(29.3)
17(24.6)
17(36.2)
 
18(48.6)
16(20.3)
 
Hepatic abscess
   
0.635
  
0.003
 No
89(76.7)
54(78.3)
35(74.5)
 
22(59.5)
67(84.8)
 
 Yes
27(23.3)
15(21.7)
12(25.5)
 
15(40.5)
12(15.2)
 
Pulmonary abscess
   
0.126
  
0.550
 No
113(97.4)
69(100.0)
44(93.6)
 
37(100.0)
76(96.2)
 
 Yes
3(2.6)
0(0.0)
3(6.4)
 
0(0.0)
3(3.8)
 
Bacteremia
   
0.000
  
0.248
 No
76(65.5)
57(82.6)
19(40.4)
 
27(73.0)
49(62.0)
 
 Yes
40(34.5)
12(17.4)
28(59.6)
 
10(27.0)
30(38.0)
 
Infection lesion 1
   
1.000
  
0.007
 Localized intrahepatic lesion
11(9.5)
7(10.1)
4(8.5)
 
8(21.6)
3(3.8)
 
 Extrahepatic lesion involved
105(90.5)
62(89.9)
43(91.5)
 
29(78.4)
76(96.2)
 
Infection lesion 2
   
0.001
  
0.112
 Localized intrapulmonary lesion
50(43.1)
39(56.5)
11(23.4)
 
12(32.4)
38(48.1)
 
 Extrapulmonary lesion involved
66(56.9)
30(43.5)
36(76.6)
 
25(67.6)
41(51.9)
 
Number of lesions
   
0.000
  
0.098
 Single lesion
72(62.1)
54(78.3)
18(38.3)
 
27(73.0)
45(57.0)
 
 Multiple lesions
44(37.9)
15(21.7)
29(61.7)
 
10(27.0)
34(43.0)
 
Number of pathogens
   
0.454
  
0.002
 Single pathogen
81(69.8)
50(72.5)
31(66.0)
 
33(89.2)
48(60.8)
 
 Multiple pathogens
35(30.2)
19(27.5)
16(34.0)
 
4(10.8)
31(39.2)
 
CRRT duration(d)
0.00(0.00,1.28)
0.00(0.00,0.00)
0.75(0.00,4.92)
0.000
0.00(0.00,0.00)
0.00(0.00,3.13)
0.000
Vasopressors duration(d)
0.00(0.00,4.24)
0.00(0.00,0.00)
5.00(1.74,10.00)
0.000
0.00(0.00,0.00)
1.79(0.00,7.92)
0.000
Mechanical ventilator duration(d)
2.07(0.00,8.79)
0.75(0.00,6.17)
4.71(1.38,10.96)
0.002
0.00(0.00,0.00)
5.17(1.75,10.96)
0.000
Length of ICU stay(d)
6.50(0.25,15.00)
3.00(0.00,11.50)
7.00(4.00,17.00)
0.002
0.00(0.00,1.50)
9.00(4.00,18.00)
0.000
Length of hospital stay(d)
18.00(9.00,33.00)
20.00(11.00,32.00)
17.00(5.00,33.00)
0.115
17.00(10.50,23.50)
23.00(7.00,37.00)
0.198

Risk factors associated with disease severity

Risk factors associated with septic shock

47/116 (40.5%) patients developed septic shock. The median diagnosis time was 11.63 (4.00, 26.00) hours after admission. There were no significant differences in smoking, alcohol consumption, hepatopathy, CKD or cardiovascular disease between the non-septic shock and septic shock cohorts (P > 0.05). Compared with patients with non-septic shock, the septic shock patients had higher levels of APACHE II score, CRP, PCT, TBIL, DBIL, GLU, CREA, PT and APTT, but lower levels of LYMPH#, MONO#, PLT and ALB. Additionally, there were significantly higher proportions of septic shock patients with CAP, diabetes, bacteremia, extrapulmonary lesion involved, multiple lesions than non-septic shock patients. Regarding clinical outcomes, the septic shock group had longer CRRT duration, vasopressors duration, mechanical ventilator duration and length of ICU stay. (Table 1).
Univariate analysis showed that APACHE II score, PLT, CRP, PCT, ALB, GLU, PT, APTT, CAP, diabetes, bacteremia, extrapulmonary lesion involved and multiple lesions were risk factors for septic shock. Multivariate logistic analysis showed that increased APACHE II score [odds ratio (OR) = 1.146; 95% confidence interval (CI), 1.059–1.240], decreased ALB level (OR = 0.867; 95%CI, 0.758–0.990), diabetes (OR = 9.591; 95%CI, 1.766–52.075) and high PCT level (OR = 1.051; 95%CI, 1.005–1.099) were independent risk factors for septic shock (Fig. 2).
To assess the probability of septic shock, a nomogram with the independent risk factors was constructed, and the calibration curves of the nomogram showed high consistencies between the predicted and actual septic shock probability (Fig. 3A, B). To further investigate the validation of the nomogram, we calculated the septic shock predicted score and drew a correlation analysis scatter plot with CRRT duration, vasopressors duration, mechanical ventilator duration and length of ICU stay respectively. Positive correlations between predicted scores and indices of CRRT, vasopressor, mechanical ventilator and ICU were observed (Fig. 3C-F).

Risk factors associated with ARDS

Seventy-nine (68.1%) patients developed ARDS. The median diagnosis time was 26.00 (18.17, 41.50) hours after admission. No significant difference was found in terms of smoking, alcohol consumption, diabetes, hepatopathy or CKD between the non-ARDS and ARDS groups (P > 0.05). Compared with non-ARDS patients, ARDS patients had higher levels of APACHE II score, GLU, PT and APTT, but lower levels of MONO#, ALB. The ARDS patients had significantly higher proportions of CAP, cardiovascular disease, abscesses, hepatic abscesses and extrahepatic lesion involved and multiple pathogens. For clinical outcomes, the ARDS patients had longer CRRT duration, vasopressors duration, mechanical ventilator duration and length of ICU stay. (Table 1).
Univariate analysis showed that APACHE II score, ALB, GLU, PT, APTT, CAP, cardiovascular disease, abscess, hepatic abscess, extrahepatic lesion involved and multiple pathogens were significantly associated with ARDS. Multivariate logistic analysis showed that increased APACHE II score (OR = 1.254; 95% CI, 1.110–1.147), community-acquired pneumonia (CAP) (OR = 11.880; 95% CI, 2.524–55.923), and extrahepatic lesion involved (OR = 14.718; 95% CI, 1.005–215.502) were the independent risk factors for ARDS (Fig. 4).
To assess the probability of ARDS, a nomogram with the independent risk factors was constructed, and the calibration curves of the nomogram showed high consistencies between the predicted and actual ARDS probability (Fig. 5A, B). To further validate the nomogram, we calculated the ARDS predicted score and drew a correlation analysis scatter plot with CRRT duration, vasopressors duration, mechanical ventilator duration and length of ICU stay. Positive correlations between predicted scores and indices of CRRT, vasopressors, mechanical ventilator and ICU were observed (Fig. 5C-F).

Risk factors associated with 30-day mortality

The 30-day all-cause mortality rate in patients with hvKP infections was 28.4% (33/116). There were no significant differences in the percentages of diabetes, hepatopathy, CKD and cardiovascular disease between the survivors and non-survivors (P > 0.05). Compared with survivors, levels of APACHE II score, CREA, PT and APTT were higher in non-survivors, while the level of ALB was lower. Furthermore, our results revealed that the non-survivors group had significantly higher proportions of smoking, alcohol consumption, CAP, septic shock and ARDS. The non-survivors had longer CRRT duration, vasopressors duration and mechanical ventilator duration, whereas the length of hospital stay was shorter for non-survivors. (Table 2).
Table 2
Clinical variables associated with 30-day mortality of hvKP infections
Characteristics
Total n = 116
Survivors n = 83(71.6%)
Non-survivors n = 33(28.4%)
P value
(Mean ± SD) or Median (IQR) or n (%)
Age(y)
55.94 ± 15.93
56.65 ± 15.17
54.15 ± 17.82
0.448
Gender
   
0.527
 Female
33(28.4)
25(30.1)
8(24.2)
 
 Male
83(71.6)
58(69.9)
25(75.8)
 
Smoking
   
0.035
 No
89(76.7)
68(81.9)
21(63.6)
 
 Yes
27(23.3)
15(18.1)
12(36.4)
 
Alcohol consumption
0.004
 No
96(82.8)
74(89.2)
22(66.7)
 
 Yes
20(17.2)
9(10.8)
11(33.3)
 
CAP
   
0.008
 No
35(30.2)
31(37.3)
4(12.1)
 
 Yes
81(69.8)
52(62.7)
29(87.9)
 
Diabetes
   
0.315
 No
75(64.7)
56(67.5)
19(57.6)
 
 Yes
41(35.3)
27(32.5)
14(42.4)
 
Hepatopathy
   
0.275
 No
79(68.1)
59(71.1)
20(60.6)
 
 Yes
37(31.9)
24(28.9)
13(39.4)
 
CKD
   
0.856
 No
108(93.1)
78(94.0)
32(90.9)
 
 Yes
8(6.9)
5(6.0)
3(9.1)
 
Cardiovascular disease
0.881
 No
62(53.4)
44(53.0)
18(54.5)
 
 Yes
54(46.6)
39(47.0)
15(45.5)
 
Septic shock
   
0.000
 No
69(59.5)
61(73.5)
8(24.2)
 
 Yes
47(40.5)
22(26.5)
25(75.8)
 
ARDS
   
0.000
 No
37(31.9)
36(43.4)
1(3.0)
 
 Yes
79(68.1)
47(56.6)
33(97.0)
 
APACHE II score
20.04 ± 12.51
14.37 ± 8.15
34.30 ± 9.95
0.000
WBC (109/L)
14.88(9.58,20.98)
14.93(10.50,17.73)
14.44(6.99,25.98)
0.947
NEUT# (109/L)
12.45(7.87,17.22)
12.75(9.03,15.51)
10.64(4.84,22.65)
0.632
LYMPH# (109/L)
1.03(0.55,1.77)
1.06(0.60,1.77)
1.01(0.44,1.79)
0.515
MONO# (109/L)
0.67(0.29,0.99)
0.70(0.35,1.01)
0.40(0.11,0.93)
0.076
HGB (g/L)
127.50(112.00,141.00)
126.00(113.00,140.00)
128.00(111.50,146.50)
0.446
PLT (109/L)
207.50(122.00,303.25)
204.00(142.00,301.00)
211.00(94.50,308.50)
0.621
CRP(mg/L)
101.62(6.14,184.64)
105.79(5.00,192.48)
96.93(7.62,164.88)
0.960
PCT(ng/mL)
3.33(0.22,30.83)
2.03(0.11,26.48)
14.22(1.065,36.49)
0.064
ALT(U/L)
44.75(19.38,75.08)
37.00(18.63,74.23)
50.00(25.18,78.55)
0.185
AST(U/L)
38.0(23.5,89.75)
36.00(22.85,75.00)
48.00(29.9,152.00)
0.101
TBIL(μmol/L)
13.40(9.48,21.98)
13.10(9.60,19.16)
14.00(8.50,28.25)
0.520
DBIL(μmol/L)
5.89(3.70,10.64)
5.46(3.35,9.90)
7.34(4.75,12.60)
0.109
ALB(g/L)
34.35 ± 6.86
35.67 ± 6.82
31.16 ± 5.94
0.002
GLU(mmol/L)
9.42(6.92,15.90)
9.01(6.95,13.00)
11.76(6.16,18.25)
0.391
CREA(μmoI/L)
86.50(66.45,135.64)
81.90(62.55,113.95)
112.45(83.34,252.23)
0.007
PT(s)
12.40(11.40,14.10)
12.00(10.98,13.73)
13.6(11.65,16.10)
0.006
APTT(s)
30.70(24.30,42.30)
28.85(23.65,35.28)
37.40(28.65,45.95)
0.005
FIB(g/L)
4.13(2.80,6.13)
4.36(2.80,6.26)
3.93(2.57,5.93)
0.620
PO2(mmHg)
100.50(74.38,136.63)
99.40(74.00,138.10)
101.00(78.90,135.90)
0.965
PCO2(mmHg)
33.55(26.98,40.53)
33.80(29.55,38.15)
33.00(24.10,47.70)
0.709
OI
242.00(158.96,324.17)
251.67(160.00,330.30)
218.30(155.17,306.52)
0.279
FiO2(%)
51.02 ± 16.80
48.87 ± 15.73
55.11 ± 18.26
0.112
PEEP(cmH2O)
6.49 ± 2.81
6.43 ± 2.54
6.57 ± 3.28
0.885
LAC(mmol/L)
2.40(1.68,4.88)
2.13(1.61,4.17)
2.73(1.09,5.46)
0.166
Abscess
   
0.010
 No
82(70.7)
53(63.9)
29(87.9)
 
 Yes
34(29.3)
30(36.1)
4(12.1)
 
Hepatic abscess
   
0.073
 No
89(76.7)
60(72.3)
29(87.9)
 
 Yes
27(23.3)
23(27.7)
4(12.1)
 
Pulmonary abscess
1.000
 No
113(97.4)
81(97.6)
32(97.0)
 
 Yes
3(2.6)
2(2.4)
1(3.0)
 
Bacteremia
   
0.117
 No
76(65.5)
58(69.9)
18(54.5)
 
 Yes
40(34.5)
25(30.1)
15(44.5)
 
Infection lesion 1
0.252
 Localized intrahepatic lesion
11(9.5)
10(12.0)
1(3.0)
 
 Extrahepatic lesion involved
105(90.5)
73(88.0)
32(97.0)
 
Infection lesion 2
0.926
 Localized intrapulmonary lesion
50(43.1)
36(43.4)
14(42.4)
 
 Extrapulmonary lesion involved
66(56.9)
47(56.6)
19(57.6)
 
Number of lesions
0.057
 Single lesion
72(62.1)
56(67.5)
16(48.5)
 
 Multiple lesions
44(37.9)
27(32.5)
17(51.5)
 
Number of pathogens
0.172
 Single pathogen
81(69.8)
61(73.5)
20(60.6)
 
 Multiple pathogens
35(30.2)
22(26.5)
13(39.4)
 
Initial antimicrobial regimens
0.009
 Penicillin/third-generation cephalosporins
12(10.5)
11(13.6)
1(3.0)
 
 (Penicillin/third-generation cephalosporins) + beta-lactamase inhibitor
49(43.0)
33(40.7)
16(48.5)
 
 Carbapenems
24(21.1)
15(18.5)
9(27.3)
 
 Quinolones/second-generation cephalosporins
13(11.4)
11(13.6)
2(6.1)
 
 (Penicillin/third-generation cephalosporins) + (quinolones/aminodycosides)
4(3.5)
2(2.5)
2(6.1)
 
 (Penicillin/third-generation cephalosporins) + beta-lactamase inhibitor + (quinolones/aminodycosides)
9(7.9)
9(11.1)
0(0.0)
 
 Carbapenems + quinolones
3(2.6)
0(0.0)
3(9.1)
 
Number of antimicrobials
0.632
 Single antimicrobial
101(88.6)
73(90.1)
28(84.8)
 
 Combined antimicrobials
13(11.4)
8(9.9)
5(15.2)
 
CRRT duration(d)
0.00(0.00,1.28)
0.00(0.00,0.00)
0.75(0.00,3.56)
0.001
Vasopressors duration(d)
0.00(0.00,4.24)
0.00(0.00,1.08)
3.87(1.39,8.40)
0.000
Mechanical ventilator duration(d)
2.07(0.00,8.79)
0.75(0.00,8.79)
4.67(1.77,8.86)
0.005
Length of ICU stay(d)
6.50(0.25,15.00)
7.00(0.00,17.00)
5.00(2.50,10.50)
0.926
Length of hospital stay(d)
18.00(9.00,33.00)
24.00(15.00,37.00)
6.00(3.00,12.00)
0.000
As shown in Fig. 6, APACHE II score, ALB, smoking, alcohol consumption, CAP, septic shock, ARDS, abscess and initial antimicrobial regimens were significantly associated with 30-day mortality. According to multivariate analysis results, younger age [hazard ratio (HR) = 0.947; 95% CI, 0.923–0.973)], increased APACHE II score (HR = 1.157; 95% CI, 1.110–1.207), and lower ALB (HR = 0.924; 95% CI, 0.869–0.983) were independent risk factors for 30-day mortality.
We constructed a nomogram of 7-day and 30-day mortality and the calibration curves showed high consistencies between the predicted and actual mortality (Fig. 7A-C). Furthermore, to compare the predictive effects of the prognostic model of 30-day mortality and the APACHE II score, we drew ROC curves of the survival predicted score and APACHE II score. The results showed that the cut-off value of the survival predicted score was 88.765 [area under the curve (AUC) 0.951, specificity 0.792, sensitivity 0.967], and the cut-off value of the APACHE II score was 19.5 (AUC 0.944, sensitivity 0.778, specificity 1.000) (Fig. 7D). We divided the survival predicted score into low-risk group (survival predicted score < 88.765) and high-risk group (survival predicted score ≥ 88.765), and then drew a K-M curve, which showed that the survival rate of the high-risk group was significantly lower than that of the low-risk group (34.1% vs. 98.3%, p < 0.0001) (Fig. 7E).

Discussions

hvKP infections have emerged as a major clinical and public health threat over the past decade [1, 10, 18, 19]. As clinical manifestations of hvKP infections are nonspecific, and differentiation of hvKP strains is mainly based on phenotypic and genotypic features without universal standards, it is difficult to identify hvKP infections early. Currently, knowledge of risk factors for disease severity and mortality remains limited. Few studies have investigated the risk factors for mortality in patients with hvKP infections, while no study has explored the risk factors or a prognostic model for disease severity. Since genetic testing is not easy to perform clinically, we summarize the routine clinical parameters to investigate risk factors associated with disease severity and 30-day mortality and construct the prognostic models. This may be the first study to report the risk factors for the severity of hvKP infection, and prognostic models of disease severity and 30-day mortality clinically.
Some hvKP-infected patients develop septic shock, ARDS. In our study, the median diagnosis times of septic shock and ARDS were 11.63 (4.00, 26.00) and 26.00 (18.17, 41.50) hours after admission, respectively. Furthermore, patients with septic shock and ARDS had longer CRRT duration, vasopressor duration, mechanical ventilator duration and length of ICU stay. The results suggest that septic shock and ARDS are reasonable predictors for assesssing disease severity in patients with hvKP infections.
Multivariate logistic analysis showed that increased APACHE II score, lower ALB, diabetes, high PCT were independent risk factors for septic shock. Studies on the correlation between APACHE II score and severity of hvKP infections have not been found. A sepsis patient’s serum ALB can decrease due to various factors including hypermetabolic state, gastrointestinal dysfunction, capillary leakage [20]. There is a causal relationship between hypoalbuminemia and an increased risk of primary and secondary infections, hypoalbuminemia has an effect on the pharmacokinetics and pharmacodynamics of anti-infective drugs, which in turn affects the clinical outcome of infections [21]. Hematocrit (HCT)-ALB difference can be a potential predictor for the prognosis of elderly sepsis patients [20]. In addition, lower ALB is a risk factor for elderly people with bacterial infections [22], and early infusion of albumin seems to reduce the mortality of patients with sepsis [23, 24]. ALB replacement in addition to crystalloids improves the haemodynamics of patients with severe sepsis during the first 7 days [25]. Ongoing research on the ALB administration supports the potential for ALB to improve sepsis survival [23]. Therefore, it is suggested that correcting hypoalbuminemia possibly reduces the risk of hvKP infections progressing to septic shock, and improves the clinical outcome of hvKP infections. Diabetes mellitus is considered a significant risk factor for acquiring hvKP infections [2629], which primarily affects male individuals aged 55–60 years [30]. Diabetes is an independent risk factor for KP pyogenic liver abscess [31], as poor glycaemic control might impair neutrophil phagocytosis and promote pathogen growth in tissues, while metabolic disturbances might negatively affect the liver [32, 33]. Moreover, diabetes, which is more likely to progress hvKP infections, especially hvKP-bloodstream infections (BSIs) [26, 28], is an independent risk factor for hvKP-BSIs [12]. No studies have been found on the association of PCT with the severity of hvKP infections. However, the PCT level has been shown to be significantly higher in hvKP group compared with cKP group [34]. And PCT has been a prognostic biomarker in patients with severe sepsis and septic shock [35]. In addition, serum procalcitonin ≥ 5 ng/mL was found to be associated with 30-day mortality of carbapenem-resistant KP infections [36]. Our research shows that PCT [19.07(2.95,42.01) ng/mL] is an independent risk factor for septic shock, indicating that PCT is one of the factors predicting the risk of septic shock in patients with hvKP infections.
Multivariate analysis showed that increased APACHE II score, CAP and extrahepatic lesion involved were independent risk factors for ARDS. The APACHE II score is also an independent factor predicting septic shock, so the APACHE II score is very important for the evaluation of hvKP infections. Furthermore, hvKP infections are usually community-acquired [3, 37, 38], CAP has been showed to be associated with high mortality in patients with hvKP infections [39]. Patients with KP pyogenic liver abscesses with sepsis have higher rates of septic shock and acute respiratory distress syndrome [40]. Severe hvKP infections with pyogenic liver abscesses in healthy adults have been reported previously [10, 31, 37, 41]. Moreover, liver abscess is a significant risk factor for hvKP infecitons [42], and abscess has been identified as an independent predictor for associated with hvKP-BSIs [43]. Nevertheless, our study revealed that hvKP infections with extrahepatic lesion involved were more serious (OR = 14.718), which seems to be inconsistent with previous results. Usually, due to the good permeability of the hepatic sinusoid of the liver, it can promote material exchange between liver cells and blood flow, which is more likely to cause bacteraemia and accelerate the spread of lesions. When the foci of hvKP infections is limited to the liver, which may be related to the immune function of the liver. As a line of defence for immunity, the liver causes a localized lesion and reduces the transfer and dissemination of bacteria to a certain extent, thus reducing the occurrence of bacteraemia and the progression of ARDS.
We constructed prognostic models to assess disease severity, validated the effects of these models, and performed correlation analyses between model scores and clinical outcomes including CRRT duration, vasopressors duration, mechanical ventilator duration and length of ICU stay. Since there were not enough additional cases, only internal validation was performed in this study, and the matching degree of internal validation was good. In the correlation analysis between scores of hvKP infections severity (septic shock, ARDS) and CRRT duration, vasopressors duration, mechanical ventilator duration, and length of ICU stay, the correlation coefficient R (0.43–074) indicated that the correlation was moderately positive. Therefore, septic shock and ARDS are suitable as observation indicators for evaluating the condition of hvKP infections. Due to the small sample size of cases included in this study, further clinical research is needed for verification.
The 30-day all-cause mortality of hvKP-infected patients in our study was 28.4%, which is close to previously reported data (4.5%-37.1%) [12, 4447]. In our study, younger age, increased APACHE II score, and decreased ALB level were independent risk factors for 30-day mortality. It has been noted that the detection rate of hvKP among the KP isolates increases in the elderly individuals, indicating that ageing can be an elevated risk for hvKP infections [26, 28, 48, 49], but age is not statistically significant for hypermucoviscous KP infections [27]. The median age of nonsurvivors in our study was 54.15 ± 17.82 years, and younger age was a risk factor for increased mortality, which may be contributed by the violent inflammatory reaction in young people, most of whom showed multiple organ dysfunction, septic shock and ARDS. This point also reminds clinicians that in the face of hvKP infections in young and middle-aged adults, modulating the host immune response may be an effective regimen to reduce mortality. In our study, the APACHE II score (HR = 1.157) in the nonsurvivors group was 34.30 ± 9.95. Previous studies have shown that a higher APACHE II score is correlated with a higher 30-day all-cause mortality rate of hvKP infections [12, 50], which is consistent with our findings. A low ALB level predicts worse outcomes for patients with BSIs caused by Enterobacteriaceae [51], and for mortality in liver transplant recipients with gram-negative bacilli (GNB) bacteraemia [52].These results are consistent with our findings.
Although most hvKP strains are rarely resistant to common antimicrobials, antibiotic-resistant hvKP isolates have been increasing over the past few years, and no literature has yet reported which antimicrobial is the most effective [4, 5355]. In our study, the initial antimicrobial regimens included piperacillin/third-generation of cephalosporins (10.5%), piperacillin/third-generation of cephalosporins combined with beta-lactamase inhibitor (43.0%), carbapenems (21.1%), quinolones/second-generation of cephalosporins (11.4%), piperacillin/third-generation cephalosporins combined with quinolones/aminodycosides (3.5%), piperacillin/third-generation cephalosporins combined with beta-lactamase inhibitor and quinolones/aminodycosides (7.9%), and carbapenems combined with quinolones (2.6%). Multiple comparisons have revealed that carbapenems combined with quinolones had higher mortality rates than piperacillin/third-generation cephalosporins combined with beta-lactamase inhibitor and quinolones/aminodycosides (100.0% vs. 0.0%, P = 0.005) and piperacillin/third-generation cephalosporins (100.0% vs. 8.3%, P = 0.009). Univariate analysis suggested that, compared with the prognosis of the combination of carbapenems and quinolones (HR = 1.000), piperacillin/third-generation cephalosporins (HR = 0.026; 95%CI, 0.003–0.260; P = 0.002), piperacillin/third-generation cephalosporins combined with beta-lactamase inhibitor (HR = 0.115; 95%CI, 0.031–0.422; P = 0.001), carbapenems (HR = 0.137; 95%CI, 0.035–0.538; P = 0.004) and quinolones/second-generation cephalosporins (HR = 0.049; 95%CI, 0.008–0. 308; P = 0.001) conferred better prognosis. Thus, carbapenems seem not to be the first choice for hvKP infections unless they are chosen based on drug sensitivity tests. However, due to the insufficient number of cases, further verifications in prospective studies are needed.
We constructed the prognostic models of 30-day mortality with the variables including age, APACHE II score and ALB level. According to ROC curves of the survival predicted score and APACHE II score, we took survival predicted score = 88.765 as the cut-point, and drew the K‒M curves. K‒M survival analysis showed that the 30-day mortality of the high-risk group (score ≥ 88.765) was significantly higher than that of the low-risk group (score < 88.765) (34.1% vs. 98.3%, p < 0.0001). The model not only had a good internal validation effect, but also was consistent with previous results.
There were several limitations in our research. Firstly, it was a retrospective study and the sample was quite small. In addition, it was a regional study that all the cases came from Dongguan, which was a labor-intensive city with a large inflow of young people. Finally, external validations of the prognostic models were not feasible due to a lack of additional data. Further investigations are required to confirm these results.

Conclusions

In this retrospective study, increased APACHE II score, decreased ALB, diabetes, higher PCT, CAP and extrahepatic lesion involved were identified as independent risk factors for septic shock and ARDS in patients with hvKP infections. The prognostic models constructed for disease severity with these conventional parameters, were significantly correlated with clinical outcomes, making them potentially practical for clinicians. Moreover, younger age, increased APACHE II score, and lower ALB were independent risk factors for 30-day all-cause mortality. The prediction model for 30-day mortality had a good validation effect. In summary, we constructed the prognostic models for disease severity and 30-day mortality in patients with hvKP infections, and the models were helpful for making more practical and effective therapeutic decisions.

Acknowledgements

This work was supported by Medical Research Foundation of Guangdong (Grant No. C2021111) and “Prominent Master from Overseas” project of Department of Science and Technology of Guangdong Province.

Declarations

Informed consent was obtained from all subjects and/or their legal guardian(s). The protocol for this study was approved by the Medical Ethics Committee of Binhaiwan Central Hospital of Dongguan (No. 2021014). All methods were performed in accordance with the relevant guidelines and regulations. All methods were performed in accordance with the relevant guidelines and regulations.
Not Applicable (NA).

Competing interests

The authors declare no competing interests.
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Literatur
2.
Zurück zum Zitat Maheswaranathan M, Ngo T, Rockey DC. Identification and management of the Hypervirulent invasive Klebsiella pneumoniae syndrome: a unique and distinct clinical entity. J Invest Med High Impact Case Rep. 2018;6:2324709618806552. Maheswaranathan M, Ngo T, Rockey DC. Identification and management of the Hypervirulent invasive Klebsiella pneumoniae syndrome: a unique and distinct clinical entity. J Invest Med High Impact Case Rep. 2018;6:2324709618806552.
3.
Zurück zum Zitat Shon AS, Bajwa RP, Russo TA. Hypervirulent (hypermucoviscous) Klebsiella pneumoniae: a new and dangerous breed. Virulence. 2013;4(2):107–18.PubMedPubMedCentral Shon AS, Bajwa RP, Russo TA. Hypervirulent (hypermucoviscous) Klebsiella pneumoniae: a new and dangerous breed. Virulence. 2013;4(2):107–18.PubMedPubMedCentral
4.
Zurück zum Zitat Lee CR, Lee JH, Park KS, Jeon JH, Kim YB, Cha CJ, et al. Antimicrobial resistance of Hypervirulent Klebsiella pneumoniae: epidemiology, hypervirulence-associated determinants, and resistance mechanisms. Front Cell Infect Microbiol. 2017;7:483.PubMedPubMedCentral Lee CR, Lee JH, Park KS, Jeon JH, Kim YB, Cha CJ, et al. Antimicrobial resistance of Hypervirulent Klebsiella pneumoniae: epidemiology, hypervirulence-associated determinants, and resistance mechanisms. Front Cell Infect Microbiol. 2017;7:483.PubMedPubMedCentral
5.
Zurück zum Zitat Guo MY, Liu Y, Fei B, Ren YY, Liu XW, Zhao ZJ, et al. Research progress on virulence factors of Hypervirulent Klebsiella pneumoniae. Zhonghua Yu Fang Yi Xue Za Zhi. 2021;55(11):1357–63.PubMed Guo MY, Liu Y, Fei B, Ren YY, Liu XW, Zhao ZJ, et al. Research progress on virulence factors of Hypervirulent Klebsiella pneumoniae. Zhonghua Yu Fang Yi Xue Za Zhi. 2021;55(11):1357–63.PubMed
6.
Zurück zum Zitat Karampatakis T, Tsergouli K, Behzadi P. Carbapenem-resistant Klebsiella pneumoniae: virulence factors, molecular epidemiology and latest updates in treatment options. Antibiotics (Basel, Switzerland). 2023;12(2):234.PubMed Karampatakis T, Tsergouli K, Behzadi P. Carbapenem-resistant Klebsiella pneumoniae: virulence factors, molecular epidemiology and latest updates in treatment options. Antibiotics (Basel, Switzerland). 2023;12(2):234.PubMed
7.
Zurück zum Zitat Li G, Shi J, Zhao Y, Xie Y, Tang Y, Jiang X, et al. Identification of Hypervirulent Klebsiella pneumoniae isolates using the string test in combination with Galleria mellonella infectivity. Eur J Clin Microbiol Infect Dis. 2020;39(9):1673–9.PubMed Li G, Shi J, Zhao Y, Xie Y, Tang Y, Jiang X, et al. Identification of Hypervirulent Klebsiella pneumoniae isolates using the string test in combination with Galleria mellonella infectivity. Eur J Clin Microbiol Infect Dis. 2020;39(9):1673–9.PubMed
8.
Zurück zum Zitat Russo TA, Olson R, Fang CT, Stoesser N, Miller M, MacDonald U, et al. Identification of biomarkers for differentiation of Hypervirulent Klebsiella pneumoniae from classical K. pneumoniae. J Clin Microbiol. 2018;56(9):e00776-00718.PubMedPubMedCentral Russo TA, Olson R, Fang CT, Stoesser N, Miller M, MacDonald U, et al. Identification of biomarkers for differentiation of Hypervirulent Klebsiella pneumoniae from classical K. pneumoniae. J Clin Microbiol. 2018;56(9):e00776-00718.PubMedPubMedCentral
9.
Zurück zum Zitat Yang P, Liu C, Wu Z, Zheng J, Yi J, Wu N, et al. Clinical outcomes, microbiological characteristics and risk factors for difficult-to-treat resistance to Klebsiella pneumoniae Infection. Infect Drug Resist. 2022;15:5959–69.PubMedPubMedCentral Yang P, Liu C, Wu Z, Zheng J, Yi J, Wu N, et al. Clinical outcomes, microbiological characteristics and risk factors for difficult-to-treat resistance to Klebsiella pneumoniae Infection. Infect Drug Resist. 2022;15:5959–69.PubMedPubMedCentral
10.
Zurück zum Zitat Choby JE, Howard-Anderson J, Weiss DS. Hypervirulent Klebsiella pneumoniae - clinical and molecular perspectives. J Intern Med. 2020;287(3):283–300.PubMed Choby JE, Howard-Anderson J, Weiss DS. Hypervirulent Klebsiella pneumoniae - clinical and molecular perspectives. J Intern Med. 2020;287(3):283–300.PubMed
11.
Zurück zum Zitat Lee SS, Chen YS, Tsai HC, Wann SR, Lin HH, Huang CK, et al. Predictors of septic metastatic infection and mortality among patients with Klebsiella pneumoniae liver abscess. Clin Infect Dis. 2008;47(5):642–50.PubMed Lee SS, Chen YS, Tsai HC, Wann SR, Lin HH, Huang CK, et al. Predictors of septic metastatic infection and mortality among patients with Klebsiella pneumoniae liver abscess. Clin Infect Dis. 2008;47(5):642–50.PubMed
12.
Zurück zum Zitat Li J, Ren J, Wang W, Wang G, Gu G, Wu X, et al. Risk factors and clinical outcomes of hypervirulent Klebsiella pneumoniae induced bloodstream infections. Eur J Clin Microbiol Infect Dis. 2018;37(4):679–89.PubMed Li J, Ren J, Wang W, Wang G, Gu G, Wu X, et al. Risk factors and clinical outcomes of hypervirulent Klebsiella pneumoniae induced bloodstream infections. Eur J Clin Microbiol Infect Dis. 2018;37(4):679–89.PubMed
13.
Zurück zum Zitat Saki M, Amin M, Savari M, Hashemzadeh M, Seyedian SS. Beta-lactamase determinants and molecular typing of carbapenem-resistant classic and hypervirulent Klebsiella pneumoniae clinical isolates from southwest of Iran. Front Microbiol. 2022;13:1029686.PubMedPubMedCentral Saki M, Amin M, Savari M, Hashemzadeh M, Seyedian SS. Beta-lactamase determinants and molecular typing of carbapenem-resistant classic and hypervirulent Klebsiella pneumoniae clinical isolates from southwest of Iran. Front Microbiol. 2022;13:1029686.PubMedPubMedCentral
14.
Zurück zum Zitat Liu C, Du P, Zhao J, Li B, Wang C, Sun L, et al. Phenotypic and genomic characterization of virulence heterogeneity in multidrug-resistant ST11 Klebsiella pneumoniae during inter-host transmission and evolution. Infect Drug Resist. 2020;13:1713–21.PubMedPubMedCentral Liu C, Du P, Zhao J, Li B, Wang C, Sun L, et al. Phenotypic and genomic characterization of virulence heterogeneity in multidrug-resistant ST11 Klebsiella pneumoniae during inter-host transmission and evolution. Infect Drug Resist. 2020;13:1713–21.PubMedPubMedCentral
15.
Zurück zum Zitat Azoulay E, Russell L, Van de Louw A, Metaxa V, Bauer P, Povoa P, et al. Diagnosis of severe respiratory infections in immunocompromised patients. Intens Care Med. 2020;46(2):298–314. Azoulay E, Russell L, Van de Louw A, Metaxa V, Bauer P, Povoa P, et al. Diagnosis of severe respiratory infections in immunocompromised patients. Intens Care Med. 2020;46(2):298–314.
16.
Zurück zum Zitat Di Pasquale MF, Sotgiu G, Gramegna A, Radovanovic D, Terraneo S, Reyes LF, et al. Prevalence and etiology of community-acquired pneumonia in immunocompromised patients. Clin Infect Dis. 2019;68(9):1482–93.PubMed Di Pasquale MF, Sotgiu G, Gramegna A, Radovanovic D, Terraneo S, Reyes LF, et al. Prevalence and etiology of community-acquired pneumonia in immunocompromised patients. Clin Infect Dis. 2019;68(9):1482–93.PubMed
17.
Zurück zum Zitat Lemiale V, Resche-Rigon M, Azoulay E. Early non-invasive ventilation for acute respiratory failure in immunocompromised patients (IVNIctus): study protocol for a multicenter randomized controlled trial. Trials. 2014;15:372.PubMedPubMedCentral Lemiale V, Resche-Rigon M, Azoulay E. Early non-invasive ventilation for acute respiratory failure in immunocompromised patients (IVNIctus): study protocol for a multicenter randomized controlled trial. Trials. 2014;15:372.PubMedPubMedCentral
18.
Zurück zum Zitat Harada S, Aoki K, Yamamoto S, Ishii Y, Sekiya N, Kurai H, et al. Clinical and molecular characteristics of Klebsiella pneumoniae isolates causing bloodstream infections in Japan: occurrence of Hypervirulent infections in health care. J Clin Microbiol. 2019;57(11):e01206-01219.PubMedPubMedCentral Harada S, Aoki K, Yamamoto S, Ishii Y, Sekiya N, Kurai H, et al. Clinical and molecular characteristics of Klebsiella pneumoniae isolates causing bloodstream infections in Japan: occurrence of Hypervirulent infections in health care. J Clin Microbiol. 2019;57(11):e01206-01219.PubMedPubMedCentral
19.
Zurück zum Zitat Russo TA, MacDonald U. The galleria mellonella infection model does not accurately differentiate between Hypervirulent and classical Klebsiella pneumoniae. mSphere. 2020;5(1):00850–00819. Russo TA, MacDonald U. The galleria mellonella infection model does not accurately differentiate between Hypervirulent and classical Klebsiella pneumoniae. mSphere. 2020;5(1):00850–00819.
20.
Zurück zum Zitat Wang Z, Zhang L, Li S, Xu F, Han D, Wang H, et al. The relationship between hematocrit and serum albumin levels difference and mortality in elderly sepsis patients in intensive care units-a retrospective study based on two large database. BMC Infect Dis. 2022;22(1):629.PubMedPubMedCentral Wang Z, Zhang L, Li S, Xu F, Han D, Wang H, et al. The relationship between hematocrit and serum albumin levels difference and mortality in elderly sepsis patients in intensive care units-a retrospective study based on two large database. BMC Infect Dis. 2022;22(1):629.PubMedPubMedCentral
21.
22.
Zurück zum Zitat Higashikawa T, Okuro M, Ishigami K, Mae K, Sangen R, Mizuno T, et al. Procalcitonin and albumin as prognostic biomarkers in elderly patients with a risk of bacterial infection. J Int Med Res. 2018;46(7):2606–14.PubMedPubMedCentral Higashikawa T, Okuro M, Ishigami K, Mae K, Sangen R, Mizuno T, et al. Procalcitonin and albumin as prognostic biomarkers in elderly patients with a risk of bacterial infection. J Int Med Res. 2018;46(7):2606–14.PubMedPubMedCentral
23.
24.
Zurück zum Zitat Rochwerg B, Alhazzani W, Sindi A, Heels-Ansdell D, Thabane L, Fox-Robichaud A, et al. Fluid resuscitation in sepsis: a systematic review and network meta-analysis. Ann Intern Med. 2014;161(5):347–55.PubMed Rochwerg B, Alhazzani W, Sindi A, Heels-Ansdell D, Thabane L, Fox-Robichaud A, et al. Fluid resuscitation in sepsis: a systematic review and network meta-analysis. Ann Intern Med. 2014;161(5):347–55.PubMed
25.
Zurück zum Zitat Caironi P, Tognoni G, Masson S, Fumagalli R, Pesenti A, Romero M, et al. Albumin replacement in patients with severe sepsis or septic shock. N Engl J Med. 2014;370(15):1412–21.PubMed Caironi P, Tognoni G, Masson S, Fumagalli R, Pesenti A, Romero M, et al. Albumin replacement in patients with severe sepsis or septic shock. N Engl J Med. 2014;370(15):1412–21.PubMed
26.
Zurück zum Zitat Li W, Sun G, Yu Y, Li N, Chen M, Jin R, et al. Increasing occurrence of antimicrobial-resistant hypervirulent (hypermucoviscous) Klebsiella pneumoniae isolates in China. Clin Infect Dis. 2014;58(2):225–32.PubMed Li W, Sun G, Yu Y, Li N, Chen M, Jin R, et al. Increasing occurrence of antimicrobial-resistant hypervirulent (hypermucoviscous) Klebsiella pneumoniae isolates in China. Clin Infect Dis. 2014;58(2):225–32.PubMed
27.
Zurück zum Zitat Guo Y, Wang S, Zhan L, Jin Y, Duan J, Hao Z, et al. Microbiological and clinical characteristics of hypermucoviscous Klebsiella pneumoniae isolates associated with invasive infections in China. Front Cell Infect Microbiol. 2017;7:24.PubMedPubMedCentral Guo Y, Wang S, Zhan L, Jin Y, Duan J, Hao Z, et al. Microbiological and clinical characteristics of hypermucoviscous Klebsiella pneumoniae isolates associated with invasive infections in China. Front Cell Infect Microbiol. 2017;7:24.PubMedPubMedCentral
28.
Zurück zum Zitat Liu C, Guo J. Hypervirulent Klebsiella pneumoniae (hypermucoviscous and aerobactin positive) infection over 6 years in the elderly in China: antimicrobial resistance patterns, molecular epidemiology and risk factor. Ann Clin Microbiol Antimicrob. 2019;18(1):4.PubMedPubMedCentral Liu C, Guo J. Hypervirulent Klebsiella pneumoniae (hypermucoviscous and aerobactin positive) infection over 6 years in the elderly in China: antimicrobial resistance patterns, molecular epidemiology and risk factor. Ann Clin Microbiol Antimicrob. 2019;18(1):4.PubMedPubMedCentral
29.
Zurück zum Zitat Li L, Yuan Z, Chen D, Xie X, Zhang B. Clinical and microbiological characteristics of invasive and Hypervirulent Klebsiella pneumoniae Infections in a teaching hospital in China. Infect Drug Resist. 2020;13:4395–403.PubMedPubMedCentral Li L, Yuan Z, Chen D, Xie X, Zhang B. Clinical and microbiological characteristics of invasive and Hypervirulent Klebsiella pneumoniae Infections in a teaching hospital in China. Infect Drug Resist. 2020;13:4395–403.PubMedPubMedCentral
30.
Zurück zum Zitat Shao C, Xin L, Mi P, Jiang M, Wu H. Phenotypic and molecular characterization of K54-ST29 Hypervirulent Klebsiella pneumoniae causing multi-system infection in a patient with diabetes. Front Microbiol. 2022;13: 872140.PubMedPubMedCentral Shao C, Xin L, Mi P, Jiang M, Wu H. Phenotypic and molecular characterization of K54-ST29 Hypervirulent Klebsiella pneumoniae causing multi-system infection in a patient with diabetes. Front Microbiol. 2022;13: 872140.PubMedPubMedCentral
31.
Zurück zum Zitat Lin YC, Cao X, Mo YC, Xie CP, Zhang YF, Li N, et al. Successful treatment of hypervirulent Klebsiella pneumoniae bacteremia with combination carbapenem and rifampicin. IDCases. 2021;26: e01276.PubMedPubMedCentral Lin YC, Cao X, Mo YC, Xie CP, Zhang YF, Li N, et al. Successful treatment of hypervirulent Klebsiella pneumoniae bacteremia with combination carbapenem and rifampicin. IDCases. 2021;26: e01276.PubMedPubMedCentral
32.
Zurück zum Zitat Foo NP, Chen KT, Lin HJ, Guo HR. Characteristics of pyogenic liver abscess patients with and without diabetes mellitus. Am J Gastroenterol. 2010;105(2):328–35.PubMed Foo NP, Chen KT, Lin HJ, Guo HR. Characteristics of pyogenic liver abscess patients with and without diabetes mellitus. Am J Gastroenterol. 2010;105(2):328–35.PubMed
33.
Zurück zum Zitat Lin JC, Siu LK, Fung CP, Tsou HH, Wang JJ, Chen CT, et al. Impaired phagocytosis of capsular serotypes K1 or K2 Klebsiella pneumoniae in type 2 diabetes mellitus patients with poor glycemic control. J Clin Endocrinol Metab. 2006;91(8):3084–7.PubMed Lin JC, Siu LK, Fung CP, Tsou HH, Wang JJ, Chen CT, et al. Impaired phagocytosis of capsular serotypes K1 or K2 Klebsiella pneumoniae in type 2 diabetes mellitus patients with poor glycemic control. J Clin Endocrinol Metab. 2006;91(8):3084–7.PubMed
34.
Zurück zum Zitat Yang F, Wang L, Zhao Q, Wu J, Jiang L, Sheng L, et al. Epidemiological features of Klebsiella pneumoniae infection in the hepatobiliary system of patients in Yantai, China, based on clinical and genetic analyses. Infect Drug Resist. 2022;15:3427–36.PubMedPubMedCentral Yang F, Wang L, Zhao Q, Wu J, Jiang L, Sheng L, et al. Epidemiological features of Klebsiella pneumoniae infection in the hepatobiliary system of patients in Yantai, China, based on clinical and genetic analyses. Infect Drug Resist. 2022;15:3427–36.PubMedPubMedCentral
35.
Zurück zum Zitat Huang MY, Chen CY, Chien JH, Wu KH, Chang YJ, Wu KH, et al. Serum procalcitonin and procalcitonin clearance as a prognostic biomarker in patients with severe sepsis and septic shock. Biomed Res Int. 2016;2016:1758501.PubMedPubMedCentral Huang MY, Chen CY, Chien JH, Wu KH, Chang YJ, Wu KH, et al. Serum procalcitonin and procalcitonin clearance as a prognostic biomarker in patients with severe sepsis and septic shock. Biomed Res Int. 2016;2016:1758501.PubMedPubMedCentral
36.
Zurück zum Zitat Chen J, Yang Y, Yao H, Bu S, Li L, Wang F, et al. Prediction of prognosis in adult patients with Carbapenem-resistant Klebsiella pneumoniae infection. Front Cell Infect Microbiol. 2021;11: 818308.PubMed Chen J, Yang Y, Yao H, Bu S, Li L, Wang F, et al. Prediction of prognosis in adult patients with Carbapenem-resistant Klebsiella pneumoniae infection. Front Cell Infect Microbiol. 2021;11: 818308.PubMed
37.
Zurück zum Zitat Siu LK, Yeh KM, Lin JC, Fung CP, Chang FY. Klebsiella pneumoniae liver abscess: a new invasive syndrome. Lancet Infect Dis. 2012;12(11):881–7.PubMed Siu LK, Yeh KM, Lin JC, Fung CP, Chang FY. Klebsiella pneumoniae liver abscess: a new invasive syndrome. Lancet Infect Dis. 2012;12(11):881–7.PubMed
38.
Zurück zum Zitat Pomakova DK, Hsiao CB, Beanan JM, Olson R, MacDonald U, Keynan Y, et al. Clinical and phenotypic differences between classic and hypervirulent Klebsiella pneumonia: an emerging and under-recognized pathogenic variant. Eur J Clin Microbiol Infect Dis. 2012;31(6):981–9.PubMed Pomakova DK, Hsiao CB, Beanan JM, Olson R, MacDonald U, Keynan Y, et al. Clinical and phenotypic differences between classic and hypervirulent Klebsiella pneumonia: an emerging and under-recognized pathogenic variant. Eur J Clin Microbiol Infect Dis. 2012;31(6):981–9.PubMed
39.
Zurück zum Zitat Yamamoto H, Iijima A, Kawamura K, Matsuzawa Y, Suzuki M, Arakawa Y. Fatal fulminant community-acquired pneumonia caused by hypervirulent Klebsiella pneumoniae K2-ST86: case report. Medicine. 2020;99(21): e20360.PubMedPubMedCentral Yamamoto H, Iijima A, Kawamura K, Matsuzawa Y, Suzuki M, Arakawa Y. Fatal fulminant community-acquired pneumonia caused by hypervirulent Klebsiella pneumoniae K2-ST86: case report. Medicine. 2020;99(21): e20360.PubMedPubMedCentral
40.
Zurück zum Zitat Li S, Yu S, Peng M, Qin J, Xu C, Qian J, et al. Clinical features and development of Sepsis in Klebsiella pneumoniae infected liver abscess patients: a retrospective analysis of 135 cases. BMC Infect Dis. 2021;21(1):597.PubMedPubMedCentral Li S, Yu S, Peng M, Qin J, Xu C, Qian J, et al. Clinical features and development of Sepsis in Klebsiella pneumoniae infected liver abscess patients: a retrospective analysis of 135 cases. BMC Infect Dis. 2021;21(1):597.PubMedPubMedCentral
41.
Zurück zum Zitat Wang JH, Liu YC, Lee SS, Yen MY, Chen YS, Wang JH, et al. Primary liver abscess due to Klebsiella pneumoniae in Taiwan. Clin Infect Dis. 1998;26(6):1434–8.PubMed Wang JH, Liu YC, Lee SS, Yen MY, Chen YS, Wang JH, et al. Primary liver abscess due to Klebsiella pneumoniae in Taiwan. Clin Infect Dis. 1998;26(6):1434–8.PubMed
42.
Zurück zum Zitat Hao Z, Duan J, Liu L, Shen X, Yu J, Guo Y, et al. Prevalence of community-acquired, Hypervirulent Klebsiella pneumoniae isolates in Wenzhou, China. Microbial Drug Resist (Larchmont, NY). 2020;26(1):21–7. Hao Z, Duan J, Liu L, Shen X, Yu J, Guo Y, et al. Prevalence of community-acquired, Hypervirulent Klebsiella pneumoniae isolates in Wenzhou, China. Microbial Drug Resist (Larchmont, NY). 2020;26(1):21–7.
43.
Zurück zum Zitat Namikawa H, Yamada K, Sakiyama A, Imoto W, Yamairi K, Shibata W, et al. Clinical characteristics of bacteremia caused by hypermucoviscous Klebsiella pneumoniae at a tertiary hospital. Diagn Microbiol Infect Dis. 2019;95(1):84–8.PubMed Namikawa H, Yamada K, Sakiyama A, Imoto W, Yamairi K, Shibata W, et al. Clinical characteristics of bacteremia caused by hypermucoviscous Klebsiella pneumoniae at a tertiary hospital. Diagn Microbiol Infect Dis. 2019;95(1):84–8.PubMed
44.
Zurück zum Zitat Hwang JH, Handigund M, Hwang JH, Cho YG, Kim DS, Lee J. Clinical features and risk factors associated with 30-day mortality in patients with pneumonia caused by Hypervirulent Klebsiella pneumoniae (hvKP). Ann Lab Med. 2020;40(6):481–7.PubMedPubMedCentral Hwang JH, Handigund M, Hwang JH, Cho YG, Kim DS, Lee J. Clinical features and risk factors associated with 30-day mortality in patients with pneumonia caused by Hypervirulent Klebsiella pneumoniae (hvKP). Ann Lab Med. 2020;40(6):481–7.PubMedPubMedCentral
45.
Zurück zum Zitat Gomez-Simmonds A, Greenman M, Sullivan SB, Tanner JP, Sowash MG, Whittier S, et al. Population structure of Klebsiella pneumoniae causing bloodstream infections at a New York City tertiary care hospital: diversification of multidrug-resistant isolates. J Clin Microbiol. 2015;53(7):2060–7.PubMedPubMedCentral Gomez-Simmonds A, Greenman M, Sullivan SB, Tanner JP, Sowash MG, Whittier S, et al. Population structure of Klebsiella pneumoniae causing bloodstream infections at a New York City tertiary care hospital: diversification of multidrug-resistant isolates. J Clin Microbiol. 2015;53(7):2060–7.PubMedPubMedCentral
46.
Zurück zum Zitat Sheng Z, Li J, Chen T, Zhu Y, Yu X, He X, et al. Clinical and Microbiological Characteristics of Klebsiella pneumoniae Bloodstream Infection in a Chinese Hospital: Hypervirulent and Multiclonal. Infect Drug Resist. 2022;15:3981–90.PubMedPubMedCentral Sheng Z, Li J, Chen T, Zhu Y, Yu X, He X, et al. Clinical and Microbiological Characteristics of Klebsiella pneumoniae Bloodstream Infection in a Chinese Hospital: Hypervirulent and Multiclonal. Infect Drug Resist. 2022;15:3981–90.PubMedPubMedCentral
47.
Zurück zum Zitat Liu YM, Li BB, Zhang YY, Zhang W, Shen H, Li H, et al. Clinical and molecular characteristics of emerging hypervirulent Klebsiella pneumoniae bloodstream infections in mainland China. Antimicrob Agents Chemother. 2014;58(9):5379–85.PubMedPubMedCentral Liu YM, Li BB, Zhang YY, Zhang W, Shen H, Li H, et al. Clinical and molecular characteristics of emerging hypervirulent Klebsiella pneumoniae bloodstream infections in mainland China. Antimicrob Agents Chemother. 2014;58(9):5379–85.PubMedPubMedCentral
48.
Zurück zum Zitat Liu C, Shi J, Guo J. High prevalence of hypervirulent Klebsiella pneumoniae infection in the genetic background of elderly patients in two teaching hospitals in China. Infect Drug Resist. 2018;11:1031–41.PubMedPubMedCentral Liu C, Shi J, Guo J. High prevalence of hypervirulent Klebsiella pneumoniae infection in the genetic background of elderly patients in two teaching hospitals in China. Infect Drug Resist. 2018;11:1031–41.PubMedPubMedCentral
49.
Zurück zum Zitat Li XJ, Wang QL, Feng JC, Guan XL, Chen ZJ, Hu B. Homology analysis and clinical infection characteristics of hypervirulent Klebsiella pneumonia. Zhonghua Yu Fang Yi Xue Za Zhi. 2021;55(8):945–51.PubMed Li XJ, Wang QL, Feng JC, Guan XL, Chen ZJ, Hu B. Homology analysis and clinical infection characteristics of hypervirulent Klebsiella pneumonia. Zhonghua Yu Fang Yi Xue Za Zhi. 2021;55(8):945–51.PubMed
50.
Zurück zum Zitat Wu X, Shi Q, Shen S, Huang C, Wu H. Clinical and bacterial characteristics of Klebsiella pneumoniae affecting 30-day mortality in patients with bloodstream infection. Front Cell Infect Microbiol. 2021;11: 688989.PubMedPubMedCentral Wu X, Shi Q, Shen S, Huang C, Wu H. Clinical and bacterial characteristics of Klebsiella pneumoniae affecting 30-day mortality in patients with bloodstream infection. Front Cell Infect Microbiol. 2021;11: 688989.PubMedPubMedCentral
51.
Zurück zum Zitat Lu F, Ma D, Zhu W, Kong G, Wang X. Prognostic analysis of severe patients with bloodstream infection caused by Enterobacteriaceae bacteria. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2020;32(4):454–7.PubMed Lu F, Ma D, Zhu W, Kong G, Wang X. Prognostic analysis of severe patients with bloodstream infection caused by Enterobacteriaceae bacteria. Zhonghua Wei Zhong Bing Ji Jiu Yi Xue. 2020;32(4):454–7.PubMed
52.
Zurück zum Zitat Wan Q, Ye Q, Su T, Zhou J. The epidemiology and distribution of pathogens and risk factors for mortality in liver transplant recipients with Gram negative bacteremia. Hepatogastroenterology. 2014;61(134):1730–3.PubMed Wan Q, Ye Q, Su T, Zhou J. The epidemiology and distribution of pathogens and risk factors for mortality in liver transplant recipients with Gram negative bacteremia. Hepatogastroenterology. 2014;61(134):1730–3.PubMed
53.
Zurück zum Zitat Zhang Y, Zhao C, Wang Q, Wang X, Chen H, Li H, et al. High prevalence of Hypervirulent Klebsiella pneumoniae infection in China: geographic distribution, clinical characteristics, and antimicrobial resistance. Antimicrob Agents Chemother. 2016;60(10):6115–20.PubMedPubMedCentral Zhang Y, Zhao C, Wang Q, Wang X, Chen H, Li H, et al. High prevalence of Hypervirulent Klebsiella pneumoniae infection in China: geographic distribution, clinical characteristics, and antimicrobial resistance. Antimicrob Agents Chemother. 2016;60(10):6115–20.PubMedPubMedCentral
54.
Zurück zum Zitat Liu C, Guo J. Characteristics of ventilator-associated pneumonia due to hypervirulent Klebsiella pneumoniae genotype in genetic background for the elderly in two tertiary hospitals in China. Antimicrob Resist Infect Control. 2018;7:95.PubMedPubMedCentral Liu C, Guo J. Characteristics of ventilator-associated pneumonia due to hypervirulent Klebsiella pneumoniae genotype in genetic background for the elderly in two tertiary hospitals in China. Antimicrob Resist Infect Control. 2018;7:95.PubMedPubMedCentral
55.
Zurück zum Zitat Liao W, De Wang L, Li D, Du FL, Long D, Liu Y, et al. High prevalence of 16s rRNA methylase genes among Carbapenem-resistant Hypervirulent Klebsiella pneumoniae Isolates in a Chinese tertiary hospital. Microbial Drug Resist (Larchmont, NY). 2021;27(1):44–52. Liao W, De Wang L, Li D, Du FL, Long D, Liu Y, et al. High prevalence of 16s rRNA methylase genes among Carbapenem-resistant Hypervirulent Klebsiella pneumoniae Isolates in a Chinese tertiary hospital. Microbial Drug Resist (Larchmont, NY). 2021;27(1):44–52.
Metadaten
Titel
Prognostic models for estimating severity of disease and predicting 30-day mortality of Hypervirulent Klebsiella pneumoniae infections: a bicentric retrospective study
verfasst von
Jieen Huang
Yanzhu Chen
Ming Li
Shujin Xie
Huasheng Tong
Zhusheng Guo
Yi Chen
Publikationsdatum
01.12.2023
Verlag
BioMed Central
Erschienen in
BMC Infectious Diseases / Ausgabe 1/2023
Elektronische ISSN: 1471-2334
DOI
https://doi.org/10.1186/s12879-023-08528-x

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