Health care-associated meningitis: Clinical epidemiology and predictor for 90-day mortality

doi:10.21203/rs.3.rs-1729047/v1

Aims

To express the clinical predictor and epidemiology of patients with health care-associated meningitis (HAM) and launch a survival analysis to screen mortality predictors.

Method

A retrospective cohort analysis containing more than 70000 patients was evaluated. Clinical and microbial epidemiology, therapy and mortality of HAM patients were reviewed. Multivariable Cox proportional hazard models were applied to achieve survival analysis.

Result

900 HAM patients from 3244 cases with cerebrospinal fluid(CSF) culture positivity were selected for epidemiology and survival analysis. The 90-day mortality was 12.4% (112 of 900) in people with HAM. Hypertension, external ventricular drainage (EVD), and lumbar drainage (LD) are mortality predictors for HAM, with a hazard ratio (HR) of 2.097 (95% C.I. 1.308–3.361, P = 0.002), 1.709 (95% C.I. 1.119–2.611, P = 0.013), and 1.614 (95% C.I. 1.083–2.405, P = 0.019). Reoperation was a factor favoring mortality, with an HR of 0.615 (95% C.I. 0.397–0.951, P = 0.029). In treatment, the outcome of patients receiving three or more antibiotic combinations is better than that of patients receiving dual-drug combinations.

Conclusion

The mortality of patients with HAM was relatively high, and the most important factors related to 90-days survival were hypertension, EVD and LD and treatment with three or more antibiotics are much better.

health care-associated meningitis

predictor

90-day mortality

epidemiology

treatment

Health care-associated meningitis (HAM) is a serious complication that is often preventable[1] but continues to place a significant burden on patients’ health and the economy. The impact of HAM includes prolonged length of hospitality stay (LOS), readmissions, disability, and increased risk of treatment failure, which can result in serious outcomes and even threaten the lives of neurosurgical and neurological patients. The prevalence rate of HAM has been reported to be 0.8%-24.0%[2–4].

The burden of HAM has been evaluated in several studies, and several risk factors related to HAM have been reported, such as diabetes mellitus, external ventricular drain (EVD), lumbar drainage (LD), Glasgow coma score (GCS), craniotomy, malignancy and so on[5–7]. Nevertheless, these studies include few patient features or focus on one specific sort of operation. In addition, few studies have focused on whole-type survival analysis of HAM. Longitudinal, large-scale and comprehensive survival analyses of HAM and survival analysis have been less reported.

The primary goal of this study was to launch a survival analysis for neurosurgical patients with HAM and identify 90-day predictors for mortality. From the Chinese nosocomial meningitis database in 2020, we constructed 22 items related to patients with HAM, such as length of operation, ICU admission, EVD and so on, and we evaluate them in this study. Furthermore, the clinical epidemiology of HAM patients, microbial distribution, and the most frequent antimicrobial susceptibility tests (ASTs), was evaluated. To the best of our knowledge, this is the first large-scale and longitudinal clinical epidemiology study globally targeting patients with HAM.

Setting and Study design

We performed a 9 years retrospective cohort analysis of all patients diagnosed with HAM at Beijing Tiantan Hospital & Capital Medical University, the largest tertiary neurosurgical center in China. Individuals aged older than 14 years who underwent neurosurgery and were diagnosed with HAM were enrolled in the Chinese nosocomial meningitis database beginning in 2012.

Definitions

HAM was defined as a positive cerebrospinal fluid (CSF) culture present > 48 hours after neurosurgery, and all individuals in the study period who met the criteria were included.

Following the definition established by the Infectious Diseases Society of America (IDSA) in 2017[3] and the U.S. Centers for Disease Control (CDC) and Prevention/National Healthcare Safety Network (PNHDN)[4], HAM is defined as a positive CSF culture > 48hours after a neurosurgery or at least 2 symptoms and signs (body temperature > 38.0° C, headache, neck stiffness and meningeal or cranial nerve signs) and one or more abnormal CSF laboratory biomarkers (elevated CSF leukocyte count, protein, or descending CSF glucose concentration in comparison with the reference range); CSF Gram’s stain positive; blood-cultured positive; nonculture diagnostic laboratory CSF tests positive; or diagnostic single antibody titer immunoglobulin M or 4-fold increase in paired immunoglobulin G for an organism.

Patients with CSF with coagulase-negative Staphylococcus(CoNS), Micrococcus, Bacillus and Propionibacterium acnes cultures were excluded because of easy contamination attributes. To avoid analyzing recurrences, patients with more than one positive CSF culture were chosen as the index culture. Individuals who died within 24 h of the index CSF culture collection date were excluded, and those who were not treated because they underwent palliative care were also excluded.

Microbiological information of HAM patients included the distribution and AST. The microorganisms referred to as HAM were divided into gram-positive bacteria (GPB), gram-negative bacteria (GNB), fungi, and Mycobacterium. Microorganisms were identified by conventional biochemical methods or matrix-assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS). AST of the target bacteria shows the four most common forms of resistance, including methicillin-resistant Staphylococcus aureus (MRSA), vancomycin-resistant Enterococcus (VRE), 3rd generation cephalosporin and carbapenem-resistant phonotype. Of them, 3rd-generation cephalosporin resistance was inferred from ceftazidime and ceftriaxone resistance, carbapenem resistance was inferred from imipenem and meropenem, and methicillin resistance in S. aureus was inferred from resistance to oxacillin. Clinical Laboratory Standards Institute (CLSI) 2019 antimicrobial susceptibility testing standards were used as the judgment (resistant, intermediate, and susceptible).

From the Chinese nosocomial meningitis database, all of the individuals’ demographic data (sex and age), medical records, and clinical microbiological tests were extracted. Of them, we selected 17 variables related to mortality induced by HAM, including tumor, malignant tumor, diabetes, hypertension, length of operation, reoperation, craniotomy, surgical site, type of incision, ICU admission, CSF leakage, EVD, LD, assisted mechanical ventilation (AMV), body temperature (TEM), sepsis, and hospital-acquired pneumonia (HAP). We used three timed procedures, including LOS, infection duration, effective treatment duration, and total fees. We employed the most extreme vital signs (e.g., TEM, sepsis) after HAM occurrence for 1–3 days.

Therapy

Three categories of antibiotic usage were as follows: 1) antibiotic prophylaxis: patients received antibiotics 0.5 h before the neurosurgical operation; 2) empirical therapy: patients received antibiotics ahead of the AST result; and 3) precise therapy: patients received antibiotic therapy by AST guidance. In addition, the usage of high-grade antibiotics was evaluated.

Statistical analysis

As appropriate, χ² or Fisher’s exact tests and the t test or Mann–Whitney U test were used for descriptive statistics. To determine the clinical epidemiology of HAM, we first performed a comparative analysis of survivor and non-survivor patients’ clinical characteristics, the percentage of microbiological distribution, and the resistance characteristics were analyzed.

In the second stage, a multi-variable survival analysis was used to estimate the primary 90-day outcome of the HAM patients, as mortality beyond 90 days was considered less likely to be related to HAM. First, a univariate analysis was carried out in the two groups, and the statistical significance was judged by the P value. After that, a Cox proportional hazards model was built to establish the effect of factors with P < 0.05 on HAM mortality. From the Cox proportional hazards model, we estimated the effect of HAM on 90-day in-hospital death as the ratio (HR), which is appropriate for datasets with small amounts of administrative censoring. The HR quantifies the association between the predictors and hospital mortality risk. The results are presented as P values, HRs and 95% confidence intervals (C.I.). The statistical analysis was carried out by SPSS 20.0 software (IBM, New York, USA), and the figure was drawn by Prism 7.0 (GraphPad, San Diego, USA).

Overall, 3244 of 71909 (4.51%) patients were identified as CSF culture positive during the study period. Of them, 1775 patients were CoNS culture positive, 188 were Micrococcus, 126 were Bacillus and 37 were Propionibacterium acnes. A total of 318 cases were excluded (209 because they had only a CSF ventriculo-peritoneal shunt, 45 because of death from other causes, 24 because of discharge within 24 hours, and 40 because of incomplete clinical records). The remaining 900 HAM patients were enrolled in this study. Therein, 112 were non-survivors, and 788 were survivors. A flowchart of the included patients is depicted in Fig. 1.

Microbiology

During the targeted 900 cases, GPB-HAM accounted for 383/900 (42.6%) of all culture-positive individuals. However, the percentage of GNB-HAM cases was 55.1% (496/900). Fungus, Mycobacterium and double-species infections accounted for 1.3%, 0.2% and 0.8%, respectively. In the non-survivor group, the percentage of GPB-HAM was significantly lower than that of survivors (P < 0.05), and similarly, GNB-HAM cases accounted for a higher percentage in the non-survivor group (P < 0.05). Whole microorganism distribution is shown in Table 1, and the susceptibilities of the 4 most important AST types to the investigated antibiotics are presented in Table 2. From that, MRSA, 3rd generation cephalosporin-resistant GNB, and carbapenem-resistant GNB were significantly different in the two groupsAll of the

Table 1

Distribution of microorganisms isolated from cerebrospinal fluids of HAM patients
Microorganism	All patients (900)	Non-survivors(112)	Survivors(788)	P
Gram-positive bacteria	383(42.6%)	35(31.3%)	348(44.2%)	0.011
Staphylococcus aureus	103(11.4%)	18(16.1%)	85(10.8%)	0.112
Enterococcus faecalis	67(7.4%)	2(1.8%)	65(8.2%)	0.011
Enterococcus faecium	62(6.9%)	1(0.9%)	61(7.7%)	0.004
Streptococcus viridans	76(8.4%)	9(8.0%)	67(8.5%)	0.999
Streptococcus pneumoniae	14(1.6%)	1(0.9%)	13(1.6%)	0.999
Other positive bacteria	61(6.8%)	4(3.6%)	57(7.2%)	0.225
Gram-negative bacteria	496(55.1%)	74(66.1%)	422(53.6%)	0.015
Acinetobacter baumannii	101(11.2%)	19(17.0%)	82(10.4%)	0.053
Klebsiella pneumoniae	107(11.9%)	28(25.0%)	79(10.0%)	< 0.001
Escherichia Coli	33(3.7%)	3(2.7%)	30(3.8%)	0.788
Klebsiella aerogenes	30(3.3%)	6(5.4%)	24(3.0%)	0.252
Acinetobacter lwoffii	20(2.2%)	0(0.0%)	20(2.5%)	0.159
Pseudomonas aeruginosa	23(2.6%)	3(2.7%)	20(2.5%)	0.999
Enterobacter cloacae	19(2.1%)	4(3.6%)	15(1.9%)	0.281
Serratia marcescens	15(1.7%)	2(1.8%)	13(1.6%)	0.999
Klebsiella oxytoca	11(1.2%)	0(0.0%)	11(1.4%)	0.377
Other negative bacteria	137(15.2%)	5(4.5%)	132(16.8%)	< 0.001
Double-species infection	7(0.8%)	3(2.7%)	4(0.5%)	0.045
Fungus	12(1.3%)	0(0.0%)	12(1.5%)	0.380
Candida albicans	6(0.7%)	2(1.8%)	4(0.5%)	0.165
Candida parapsilosis	3(0.3%)	1(0.1%)	2(2.5%)	0.329
Candida tropicalis	2(0.2%)	1(0.1%)	1(0.1%)	0.234
Candida glabrata	1(0.1%)	0(0.0%)	1(0.1%)	0.999
Mycobacterium	2(0.2%)	0(0.0%)	2(0.3%)	0.999
Total	900(100.0%)	112(100.0%)	788(100.0%)	-

Table-2: 4 the most important AST of the microorganisms

Groups	All patients	Non-survivors	Survivors	P
MRSA	37(37.9%)	12(66.7%)	25(29.4%)	0.006
VRE	16(12.2%)	1(25.0%)	15(11.8%)	0.410
3-rd generation cephalosporin resistance GNB	152(36.0%)	37(50.0%)	115(27.2%)	< 0.001
Carbarpenem resistance GNB	105(24.9%)	36(48.6%)	69(16.4%)	< 0.001

Patients

Of the 900 HAM patients included in the final analysis, the mean age was 41 (27–54) years, 516 (57.3%) were men and 384 (42.7%) were women (Table 3). A total of 621 (69.0%) patients had a cerebral or spinal tumor, and 282 (31.3%) had malignant tumors. The proportions of patients who had diabetes and hypertension were 3.7% and 15.7%, respectively. Other relevant variables are shown in Table 3.

Table 3

Demographic, clinical characteristics of HAM cases and univariate analysis
Variable	All patients (900)	Non-survivors(112)	Survivors(788)	P
Basic condition
Gender	516(57.3%)	70(62.5%)	446(56.6%)	0.262
Age	41(27,54)	41(27,53)	47(31,58)	0.157
Risk factors
Diabetes	33(3.7%)	5(4.4%)	28(3.6%)	0.592
Hypertension	141(15.7%)	33(29.5%)	108(13.7%)	< 0.001
TEM(℃)	37.6 ± 0.9	37.8 ± 0.9	37.6 ± 0.9	0.025
Tumor	621(69.0%)	80(71.4%)	541(68.7%)	0.587
Malignant tumor	282(31.3%)	48(42.9%)	234(29.7%)	0.007
Length of operation(hours)	4.5 ± 3.0	5.0 ± 3.0	4.5 ± 3.0	0.062
Reoperation	199(22.1%)	39(34.8%)	160(20.3%))	0.001
Craniotomy	625(69.4%)	79(70.5%)	546(69.3%)	0.872
Surgical site				0.715
head	882(98.0%)	111(99.1%)	771(97.8%)
spine	18(2.0%)	1(0.9%)	17(2.2%)
Type of incision				0.481
clean	471(52.3%)	55(49.1%)	416(52.8%)
contamination	429(47.7%)	57(50.9%)	372(47.2%)
ICU admission	357(39.7%)	80(71.4%)	277(35.2%)	< 0.001
CSF leakage	138(15.3%)	23(10.5%)	115(14.6%)	0.122
EVD	362(40.2%)	69(61.6%)	293(37.2%)	< 0.001
LD	257(28.6%)	48(42.9%)	209(26.5%)	0.001
AMV	359(39.9%)	77(68.8%)	282(35.8%)	< 0.001
Sepsis	64.0(7.1%)	12(10.7%)	52(6.6%)	0.120
HAP	124.0(13.8%)	52(46.4%)	72(9.1%)	< 0.001
Time procedure
LOS(days)	24(18, 36)	31(21, 48)	23(17.0, 34.3)	< 0.001
Time of HAMs occurrence (days)	8(4, 13)	10.0(6.0, 16.0)	7.0(4.0, 12.0)	< 0.001
Effective treatment days (days)	5.1 ± 7.3	6.8 ± 10.5	4.9 ± 6.8	0.794
Fee(Yuan)	71099.9(50062.7, 118198.88)	137254.0(70352.5, 230416.0)	68721.8(48569.8, 106318.4)	< 0.001

From Table 3, the distribution of sex, age and comorbidities was similar between the two groups, except hypertension, which was more common in non-survivor participants (29.5% vs. 13.7%, P < 0.001). Factors, including TEM, malignant tumor, ICU admission, EVD, LD, AMV and HAP, were not evenly distributed between participants in the two groups (P < 0.05). There were more patients with malignant tumors (42.9% vs. 29.7%), reoperations (34.8% vs. 20.3%), ICU admissions (71.4% vs. 35.2%), EVD (61.6% vs. 37.2%), LD (42.9% vs. 26.9%), AMV (68.8% vs. 35.8%) and HAP (46.4% vs. 9.1%) in non-survivors than survivors. We observed no difference between the two groups in the time of HAM occurrence and effective treatment days except for the LOS (P < 0.001). In total, the median fee burden of the non-survivors was much higher than that of the survivors (137254.0 vs. 68721.8, P < 0.001).

Survival analysis

The 90-day mortality was 12.4% (112 of 900) in patients with HAM. The results of survival analysis by the Cox proportional hazards model are summarized in Fig. 2 and Fig. 3, including hypertension, reoperation, EVD and LD. Of the targeted four parameters, hypertension, EVD and LD were adverse events for HAM, with a hazard ratio (HR) of 2.097 (95% C.I. 1.308–3.361, P = 0.002), 1.709 (95% C.I. 1.119–2.611, P = 0.013), and 1.614 (95% C.I. 1.083–2.405, P = 0.019), respectively. In contrast, reoperation was a favorable factor for the occurrence of mortality caused by HAM, with an HR of 0.615 (95% C.I. 0.397–0.951, P = 0.029). All of the survival analysis raw data has been attached in the supplementary data(survival analysis raw data).

Therapy

A total of 77.6% (698/900) of patients received antibiotic prophylaxis, 88.6% (797/900) acquired empirical therapy, and 85.9% (773/900) were treated with high-grade antibiotics. The percentages of mono-, dual- and triple- or more-combined antibiotics in empirical therapy were 29.4% (234/797), 48.6% (387/797) and 22.1% (176/797), respectively. Meanwhile, 85.4% (769/900) of patients underwent precise therapy, and the three antibiotic usage ratios were 18.2% (140/769), 59.9% (461/769) and 20.5% (168/769).

In terms of empirical therapy, the mortality rates of patients with triple or more antibiotic combinations and dual-drug combinations were 19.9% (35/176) and 11.6% (45/387), respectively. In precise treatment, the mortality rates of patients with triple or more antibiotic combinations and dual-drug combinations were 24.4% (41/168) and 13.4% (62/461), respectively, and the difference was statistically significant (P = 0.013, P = 0.001). The percentages of different antibiotic therapy types are shown in Fig. 4.

Neurosurgery always involves the opening of the central nervous and CSF circulatory systems and causes blood-brain barrier destruction, which easily leads to HAM. In keeping with our findings, previous studies with a smaller number of HAM patients have also found predictors to reduce the mortality ratio. However, this cohort originated from the largest HAM database in China, with the most representative epidemiology. We first summarize the clinical and microbial epidemiology in patients with HAM, and the predictors for 90-day mortality account for confounding by indication through the use of Cox propensity score modeling. To the best of our knowledge, this is the largest and longest cohort of patients with HAM in which a survival analysis has been evaluated.

The pathogens that cause HAM are mainly bacteria. In our study, the proportion of HAM cases caused by GNB was higher than that caused by GPB, and the proportion of gram-negative bacteria in the non-survivor group was significantly higher than that of the gram-positive bacteria, similar to reports in the literature[8]. The majority of multidrug-resistant bacteria, which always lead to poor outcomes in patients because of severe infection, are gram-negative bacteria, such as carbapenem-resistant Enterobacteriaceae (CRE), extended-spectrum beta-lactamase (ESBL)-producing Enterobacteriaceae, carbapenem-resistant Acinetobacter baumannii (CRAB), and carbapenem-resistant Pseudomonas aeruginosa (CRPE). There is a high mortality rate in the treatment process due to the lack of sensitive antibiotics. A follow-up study indicated that infections caused by gram-negative bacteria do have certain sequelae effects, and better antibiotic care is urgently needed[9]. Drug resistance is a very important factor in patients with infections, and a number of studies have shown that different mechanisms of drug resistance have significant differences in the outcomes of multiple infections[10]. For example, ESBL production is a critical risk factor for sepsis caused by Enterobacteriaceae[11]. Another Spanish study showed that CRE infection significantly affected the mortality of sepsis patients[12]. The results of our study showed that, except for VRE, the proportion of drug-resistant bacteria in the non-survivor group was significantly higher than that in the survivor group.

We observed significant mortality in patients with HAM, and the mortality rate in our study was higher than that in previous reports[13, 14]. HAM patients with adverse outcomes, had greater levels of hypertension, EVD and LD than those of surviving patients. Furthermore, the reoperation rate was significantly higher in surviving patients. Hypertension is one of the inducers of immune system diseases. For example, pulmonary hypertension is closely related to immune suppression[15, 16]. Compared with healthy people, the immunity of hospitalized patients has a downward trend, and hypertension will aggravate the occurrence of this situation and lead to a poor outcomes[17]. Similarly, hypertension can induce some inflammation[18], and the presence of strong inflammation in patients, such as a “cytokine storm”, will increase the difficulty of clinical treatment. Therefore, hypertension, as an independent predictor for survival in HAM patients, has a certain theoretical basis. EVD and LD are common operations in neurosurgical patients, and they have similar clinical characteristics. HAM caused by both of them are catheter-related infections. Catheter-related infections are an important cause of patient death[19], and EVD is also an independent predictor for craniotomy. Impurity eyewinker entry is one of the major causes of infection, and if antibiotics are not administered in time, bacteria will adhere to the catheter, and bacteria such as Pseudomonas aeruginosa that can form biofilms can have serious consequences[20]. As an independent protective factor, reoperation is an interesting discovery. In conventional circumstances, reoperation means that doctors cannot solve the surgical problem by a single operation, and reoperation has certain anatomical significance in the treatment of infection, such as exposing the bacterial storage site, so reoperation should be taken seriously or employed as a protective factor.

Previous reports have shown that antibiotic prophylaxis can effectively reduce infection occurrence[21]. Our research also supports this theory. Up to 80% of HAM patients received antibiotic prophylaxis. The use of antibiotic prophylaxis in the survivor group was significantly higher than that in the non-survivor group (P = 0.001). Nevertheless, empirical treatment and precise treatment and the choice of antibiotics are closely related to patient mortality.

In infectious patients, several retrospective studies targeting multiresistant bacteria have recommended improved survival in patients receiving two or more combinations of active antibiotics in vitro, mostly in patients with high risk of mortality[22]. It has been reported that compared with single drug usage, multidrug combinations can have a better cure rate for certain infections[23, 24]. However, there are also reports showing that for some special drug-resistant bacterial infections, multidrug combinations cannot achieve expected results[25, 26], as has been shown in recent clinical trials. In our study, we recommended that dual-drug combinations and triple-drug combinations are statistically significant in empirical treatment (P = 0.013) and precise treatment (P = 0.001) of HAM. The mortality rate of patients with triple drug combinations is lower. However, the synergistic effect of different antibiotics on HAM still needs further exploration and research.

Our study has some limitations. First, it is a retrospective study in a single center, although it is the largest HAM series from the China nosocomial meningitis database reported thus far. Second, the molecular epidemiology of the patients’ infections and drug resistance genes were not analyzed, which may have certain shortcomings for precision drug therapy.

In conclusion, in our study, using a Cox proportional hazards model, 4 variables, hypertension, EVD, LD and reoperation, were selected as mortality clinical predictors in patients with HAM. The clinical and microbiological epidemiology of HAM patients was expressed, and the difference between non-survivors and survivors was statistically significant. For treatment, the clinical significance of three or more drug combinations was higher than that of dual-drug usage, which may provide effective measures for the treatment of patients.

HAM: health care-associated meningitis

CSF: Cerebrospinal fluid

EVD: External ventricular drainage

LD: Lumbar drainage

HR: Hazard ratio

LOS: Length of hospitality stay

GCS: Glasgow coma score

AST: antimicrobial susceptibility test

CDC: Centers for Disease Control

PNHDN: Prevention/National Healthcare Safety Network

GPB Gram-positive bacteria

GNB: Gram-negative bacteria

MALDI-TOF MS: matrix-assisted laser desorption ionization time of flight mass spectrometry

MRSA: Methicillin-resistant Staphylococcus aureus

VRE: Vancomycin-resistant Enterococcus

CLSI: Clinical Laboratory Standards Institute

AMV: Assisted mechanical ventilation

TEM: Body temperature

HAP: hospital-acquired pneumonia

CoNS: coagulase-negative Staphylococcus

CRE: Carbapenem-resistant Enterobacteriaceae

ESBL: Extended-spectrum beta-lactamase

CRAB: carbapenem-resistant Acinetobacter baumannii

CRPE: carbapenem-resistant Pseudomonas aeruginosa

Ethics approval and consent to participate: The ethical office of Beijing Tiantan Hospital approved this study with a waiver of informed consent，and the committee’s reference number is KY2021-079-02.

Consent for publication: All of the authors consent to publish this study,

Availability of data and material: The raw data has been attached in the supplementary data.

Funding: This work was supported by a research grant from the Beijing Hospital Authority Clinical Medicine Development of Special Funding (Grant no. ZYLX202108), and Beijing Municipal Administration of Hospitals Incubating Program (grant no. PX2022021).

Competing Interests: The authors declared that no competing interests exist in this study.

Acknowledgements

The authors would like to thank all researchers for their involvement in data collection for this study, including: Yin Xiaoxuan, Yang Jing, Wang Houzhi, Zhang Yuzheng, Li Xuan, Zhao Yujing, Liu Haoran and all support staff for providing us with the opportunity to perform this study.

Author contributions

GH Zheng conceptualized and designed the study. YJ Shi performed the statistical experiments. YW Ding, JL Sun, and LY Qian collected the data. SQ Wang, and H Lv analyzed and interpreted the data. GJ Zhang conducts the whole study, which all authors reviewed and approved for publication.

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No competing interests reported.

survivalanalysisrawdata.xlsx

Health care-associated meningitis: Clinical epidemiology and predictor for 90-day mortality

Status:

Version 1

Abstract

Aims

Method

Result

Conclusion

Figures

Introduction

Method

Setting and Study design

Definitions

Therapy

Statistical analysis

Results

Microbiology

Patients

Survival analysis

Therapy

Discussion

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Supplementary Files

Status:

Version 1