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Utilisation Of Antipseudomonal β-Lactam and Predictors of Pseudomonas Infection in Patients Admitted to a Secondary Hospital in Kelantan

​

​

PRESENTING AUTHOR: FADHILAH NAJWA BINTI ISMAIL

CO-INVESTIGATORS: AZETA BINTI ABDULLAH

MUNIRAH BINTI YUSOF

PTJ: HOSPITAL TANAH MERAH

​

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INTRODUCTION

2

Pseudomonas

Pseudomonas species are gram-negative, aerobic bacilli.

Pseudomonas aeruginosa and Pseudomonas maltophilia account for approximately 80% of pseudomonas infection in human. [1]

National Healthcare Safety Network in the United States, from year 2011 to 2014, P. aeruginosa was one of the causes of; [2]

  • hospital-acquired infections (HAI) in general
  • ventilator-associated pneumonia (VAP)
  • the most common multidrug-resistant gram-negative pathogen causing VAP
  • catheter-associated urinary tract infections
  • surgical site infections
  1. Staphylococcus aureus
  2. Klebsiella pneumoniae
  3. Candida albicans
  4. Candida sp
  5. Escherichia coli
  6. Staphylococcus coagulase negative
  7. P. aeruginosa and Pseudomonas sp in combination

Most isolated microorganism from year 2017-2019 in Hospital Tanah Merah (HTM):

[1] Baron S., (1996) Medical Microbiology (4th ed.)

[2] Weiner et al., (2016) Infect Control Hosp Epidemiol, 37(11), 1288

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LITERATURE REVIEW

3

RESEARCHER

RESEARCH TITLE

STUDY FINDINGS

Al-Kabsi et al. (2011)

Antimicrobial resistance pattern of clinical isolate of Pseudomonas aeruginosa in the University of Malaya Medical Center, Malaysia

P. aeruginosa isolated from various clinical samples has showed increasing resistance to gentamicin with 94.3%, followed by ciprofloxacin (92%), ceftazidime (89.8%), imipenem (73.9%), piperacillin/tazobactam (61.4%), aztreonam (52.3%), and amikacin (50%) and only susceptible to colistin with 92%.

Ding et al. (2016)

Prevalence of Pseudomonas aeruginosa and antimicrobial-resistant Pseudomonas aeruginosa in patients with pneumonia in mainland China: a systematic review and meta-analysis

  • P. aeruginosa accounted for 19.4% of all isolates in ventilator-associated pneumonia (VAP), which was similar to the proportion in hospital-acquired pneumonia (HAP) of 17.8%.
  • P. aeruginosa exhibited varying resistance to agents recommended for the initial management of VAP
  • The prevalence of P. aeruginosa isolates resistant to agents recommended for the treatment of HAP

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LITERATURE REVIEW

4

RESEARCHER

RESEARCH TITLE

STUDY FINDINGS

Choi et al. (2018)

Clinical predictors of Pseudomonas aeruginosa bacteraemia in emergency department

  • Male, underlying solid tumor, history of hospitalization, or surgery within a year was associated with Pseudomonas bacteremia in the ED
  • Respiratory tract infection was an independent risk factor for Pseudomonas bacteremia in the ED whereas age, underlying diabetes mellitus, and presentation as urinary tract infection were negative clinical predictors for Pseudomonas bacteremia.
  • The in-hospital mortality was significantly higher in the Pseudomonas group than the E. coli group (30% versus 8%)

Angrill et al. (2020)

Determinants of Empirical Antipseudomonal Antibiotic Prescription for Adults with Pneumonia in the Emergency Department.

  • The empirical use of β-APS was significantly more frequent in immunocompromised group and Health Care-Associated Pneumonia group
  • Pseudomonas aeruginosa was isolated in 9 out of 549 (1.6%) pneumonia patients

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PROBLEM STATEMENT

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01

P. aeruginosa represents one of the most concerning pathogens involved in antibiotic resistance, and has been highlighted as one of the ESKAPE organisms by Infectious Diseases Society of America.

02

Antipseudomonal β-lactam antibiotics (β-APS) were commonly prescribed to patients, despite the isolation of Pseudomonas were only 7.5% per year among all the microorganisms isolated in HTM

03

Averagely, defined daily dose (DDD) 2017-2019 in HTM:

  • ceftazidime was 25.0 (3,000 vials per year)
  • piperacillin/tazobactam was 31.0 (18,000 vials per year)
  • cefepime was 4.6 (700 vials per year)

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RESEARCH OBJECTIVES

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GENERAL OBJECTIVE

To determine the trend of antipseudomonal β-lactam (β-APS) prescribing pattern and the predictive factors for Pseudomonas infection

SPECIFIC OBJECTIVE

1. To describe the utilization pattern and clinical characteristic of patients prescribed with β-APS in HTM.

2. To describe the microbiological characteristics and treatment of infection in patients prescribed with β-APS in HTM.

3. To identify the predictive factors of Pseudomonas infection in patients admitted to HTM

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METHODOLOGY

​

7

STUDY DESIGN

​

Prospective Cross-Sectional Observational Study

​

SAMPLING METHOD

Convenient Sampling

​

STUDY POPULATION

Adult patients ≥ 18 years old prescribed with β-APS: -Piperacillin/Tazobactam

-Ceftazidime

-Cefepime.

​

STUDY LOCATION

HTM

- Intensive care unit (ICU)

-Medical wards

-Obstetrics & Gynaecology

-Orthopedic

-Surgical

STUDY DURATION

1 October 2020 - 30 September 2021

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8

Refuse

Patient who refuse to participate in the research.

Melioidosis

Patient who is prescribed with antipseudomonal β-lactam antibiotics for Melioidosis.

STAT Dose

Patient who only given for STAT dose of β-APS.

Prophylaxis

Patient who is prescribed with β-APS for prophylaxis of surgical site infection.

EXCLUSION CRITERIA

Contaminant

Microorganisms isolated but do not fulfill criteria of infection (ie, contaminant and coloniser).

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METHODOLOGY

9

SAMPLE SIZE

CALCULATED

MARGIN ERROR

The margin of error is set at 5% with confidence intervals of 95%

SAMPLE COLLECTED : 176

Number of patients dispensed with ceftazidime, cefepime, and piperacillin/tazobactam in Hospital Tanah Merah was estimated around 600 patients per year

SAMPLE CALCULATED :

234

Sample size is calculated using the free online sample size calculator developed by Creative Research Systems, USA

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DATA COLLECTION

10

PhIS

Electronic registry of patients

Stage 1

Explain & Consent

INFORMATION SHEET AND CONSENT FORM

Stage 2

Data Collection

DATA COLLECTION FORM

Stage 3

Clinical data & progress notes

BHT, Records Unit HTM, eDelphyn

Stage 4

Data Collection Process

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STATISTICAL ANALYSIS

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Study the predictive factors

03

P-value <0.05

statistically significant

Multiple logistic regression

01

IBM® Statistical Package for Social Sciences (SPSS) version 26

02

Demographic and Clinical Characteristics Data:

​

TYPES OF VARIABLES

DATA EXPRESSION

Categorical variables

Frequencies & Percentages

Continuous Variables

Mean ± Standard Deviation (SD) or Median interquartile range (IQR)

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RESULTS & DISCUSSIONS

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Objective 1: To describe the utilization pattern and clinical characteristic of patients prescribed with antipseudomonal β-lactam (β-APS) in HTM.

13

Baseline Characteristic

n

%

Age, year

Mean ± SD

​

53.86 ± 16.74

​

Gender

Male

Female

​

59

117

​

33.5

66.5

Race

Malay

Chinese

Indian

Others

169

3

1

3

​

96.0

1.7

0.6

1.7

Antipseudomonal β-lactam

Empirical

Definitive

​

161

15

​

91.5

8.5

Table 1: Socio-demographic and clinical characteristics of patients prescribed with antipseudomonal β-lactam in HTM (n=176)

Baseline Characteristic

n

%

Co-morbidities

No

Yes

​

21

155

​

11.9

88.1

No of co-morbidities

Mean ± SD

​

1.97 ± 1.32

​

Immunodeficiency state

No

Yes

​

156

20

​

88.6

11.4

Predisposing conditions

No

Yes

​

66

110

​

37.5

62.5

No of predisposing conditions

Mean ± SD

​

​

0.93 ± 0.92

​

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Objective 1: To describe the utilization pattern and clinical characteristic of patients prescribed with antipseudomonal β-lactam (β-APS) in HTM.

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Baseline Characteristic

n

%

Hospitalization within 90 days

No

Yes

​

​

99

77

​

​

56.3

43.8

ICU admission within 90 days

No

Yes

​

​

160

16

​

​

90.9

9.1

Surgery within 90 days

No

Yes

​

166

10

​

94.3

5.7

Antimicrobial (preceding 30 days)

No

Yes

​

​

77

99

​

​

43.8

56.3

Table 1: Socio-demographic and clinical characteristics of patients prescribed with antipseudomonal β-lactam in HTM (n=176)

Baseline Characteristic

n

%

Corticosteroids exposure within 30 days

No

Yes

​

157

19

​

89.2

10.8

Parenteral nutrition exposure within 90 days

No

Yes

​

174

2

​

98.9

1.1

Presence of sepsis

No

Yes

​

93

83

​

52.8

47.2

Presence of shock

No

Yes

​

130

46

​

73.9

26.1

Patient-days (before culture sent/antipseudomonal β-lactam initiation), days

Median (IQR)

1.00 (4)

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Objective 1: To describe the utilization pattern and clinical characteristic of patients prescribed with antipseudomonal β-lactam (β-APS) in HTM.

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Chart 1: Diagnosis upon β-APS initiation

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Objective 2: To describe the microbiological characteristics and treatment of infection in patients prescribed with antipseudomonal β-lactam (β-APS) admitted to HTM.

16

Chart 2: Choice of β-APS in patients admitted to HTM

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Objective 2: To describe the microbiological characteristics and treatment of infection in patients admitted to HTM.

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Table 2: Microbiological characteristics and treatment of infection in patients admitted to HTM (n=176)

Baseline Characteristic

n

%

Number of cultures taken

Single source

Multiple source

130

46

73.9

26.1

Pathogen isolated

No growth

Monomicrobial

Polymicrobial

95

67

14

54.0

38.1

8.0

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Objective 2: To describe the microbiological characteristics and treatment of infection in patients prescribed with antipseudomonal β-lactam (β-APS) admitted to HTM.

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Chart 3: Pseudomonas isolated in patients admitted to HTM

Antipseudomonal prescriptions were common in spite of the very low incidence of Pseudomonas Aeruginosa [1]

[1] Angrill et al. 2020. BMC Pulm Med 20(1): 83.

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Objective 3: To identify the predictive factors of Pseudomonas infection in patients admitted to HTM

Table 3: Comparison of patient’s characteristics for patients with and without Pseudomonas infection (n=176)

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Characteristics

Total (N=176)

Patients without Pseudomonas (n=160)

Patients with Pseudomonas (n=16)

P value

Age, median (IQR)

55 (25)

55 (25)

52 (26)

0.696

Gender

Male, n (%)

Female, n (%)

​

59 (33.5)

117 (66.5)

​

49 (30.6)

111 (69.4)

​

10 (62.5)

6 (37.5)

​

0.010

Race

Malay, n (%)

Others, n (%)

​

169 (96)

7 (4)

​

154 (96.3)

6 (3.7)

​

15 (93.8)

1 (6.2)

0.626

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Table 3: Comparison of patient’s characteristics for patients with and without Pseudomonas infection (n=176)

Characteristics

Total (N=176)

Patients without Pseudomonas (n=160)

Patients with Pseudomonas (n=16)

P value

Diagnosis upon initiation

​

Respiratory Tract Infection

89 (50.6)

88 (55.0)

1 (6.3)

<0.001

Skin & Soft Tissue Infection

28 (15.9)

21 (13.1)

7 (43.8)

0.001

Gastrointestinal Tract Infection

14 (8.0)

12 (7.5)

2 (12.5)

0.481

Bacteraemia

10 (5.7)

8 (5.0)

2 (12.5)

0.217

Sepsis, unknown source

9 (5.1)

9 (5.6)

0 (0)

0.330

Urinary Tract Infection

8 (4.5)

5 (3.1)

3 (18.8)

0.004

Tropical Infection

7 (4.0)

7 (4.4)

0 (0)

0.393

Bone & Joint Infection

4 (2.3)

3 (1.9)

1 (6.3)

0.319

Neutropenic Sepsis

4 (2.3)

4 (2.5)

0 (0)

0.681

Others

1 (0.6)

1 (0.6)

0 (0)

0.909

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Table 3: Comparison of patient’s characteristics for patients with and without Pseudomonas infection (n=176)

Presence of comorbidities, n (%)

155 (88.1)

142 (88.8)

13 (81.3)

0.378

Cardiovascular, n (%)

96 (54.4)

88 (55.0)

8 (50.0)

0.702

Chronic Kidney Disease, n (%)

43 (24.4)

40 (25)

3 (18.8)

0.579

Respiratory Disease, n (%)

16 (9.1)

14 (8.8)

2 (12.5)

0.619

Diabetes Mellitus, n (%)

83 (47.2)

74 (46.3)

9 (56.3)

0.445

Hepatic dysfunction, n (%)

3 (1.7)

2 (1.3)

1 (6.3)

0.141

Presence of immunodeficiency state, n (%)

20 (11.4)

19 (11.9)

1 (6.3)

0.499

Characteristics

Total (N=176)

Patients without Pseudomonas (n=160)

Patients with Pseudomonas (n=16)

P value

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Table 3: Comparison of patient’s characteristics for patients with and without Pseudomonas infection (n=176)

Presence of predisposing conditions, n (%)

110 (62.5)

97 (60.6)

13 (81.3)

0.104

Hospitalisation within 90 days, n (%)

77 (43.8)

70 (43.8)

7 (43.8)

1.000

ICU admission within 90 days, n (%)

16 (9.1)

14 (8.8)

2 (12.5)

0.619

History of Surgery within 90 days, n (%)

10 (5.7)

8 (5.0)

2 (12.5)

0.217

Antimicrobial exposure preceding 30 days, n (%)

99 (56.3)

86 (53.8)

13 (81.3)

0.034

Corticosteroid exposure within 30 days, n (%)

19 (10.8)

18 (11.3)

1 (6.3)

0.539

Parenteral nutrition within 90 days, n (%)

2 (1.1)

2 (1.3)

0 (0)

0.826

Characteristics

Total (N=176)

Patients without Pseudomonas (n=160)

Patients with Pseudomonas (n=16)

P value

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Table 3: Comparison of patient’s characteristics for patients with and without Pseudomonas infection (n=176)

Characteristics

Total (N=176)

Patients without Pseudomonas (n=160)

Patients with Pseudomonas (n=16)

P value

Clinical Parameters upon ED admission

​

Systolic Blood Pressure (SBP), mean ± SD

127.94 ± 25.0

132.1 ± 31.6

127.94 ± 25.0

0.610

Diastolic Blood Pressure (DBP), mean ± SD

70.25 ± 12.0

75.31 ± 17.1

70.25 ± 12.0

0.249

Pulse Rate (PR), mean ± SD

87.38 ± 23.1

98.11 ± 19.3

87.38 ± 23.1

0.039

Respiratory Rate (RR), median (IQR)

22 (8)

22 (8)

20.5 (6)

0.052

Temperature, median (IQR)

37 (1)

37 (1.1)

37.1 (1.2)

0.805

Total White Blood Cell (TWBC), median (IQR)

13.1 (10.3)

13.2 (11.0)

11.6 (9.3)

0.494

Neutrophils, median (IQR)

10.1 (9.28)

10.1 (10.9)

9.98 (4.9)

0.808

Lymphocytes, median (IQR)

1.2 (1.89)

1.2 (1.76)

1.1 (3.4)

0.304

Platelets, mean ± SD

349.7 ± 151.0

302.2 ± 163.7

349.7 ± 151.0

0.267

C-Reactive Protein (CRP), median (IQR)

96.2 (177)

94.6 (184)

118.2 (200)

0.779

Albumin, mean ± SD

30.12 ± 9.6

30.24 ± 6.7

30.12 ± 9.6

0.952

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Table 3: Comparison of patient’s characteristics for patients with and without Pseudomonas infection (n=176)

Characteristics

Total (N=176)

Patients without Pseudomonas (n=160)

Patients with Pseudomonas (n=16)

P value

Clinical Parameters upon antipseudomonal initiation

SBP, mean ± SD

129 ± 19.4

126.43 ± 25.7

129.00 ± 19.4

0.698

DBP, mean ± SD

72.81 ± 9.5

73.56 ± 16.6

72.81 ± 9.5

0.860

PR, mean ± SD

81.56 ± 20.3

98.67 ± 19.3

81.56 ± 20.3

0.001

RR, median (IQR)

22 (8)

22 (7)

20.1 (10)

0.003

Temp, median (IQR)

37 (0.8)

37 (0.8)

37 (0.7)

0.345

TWBC, median (IQR)

13.7 (11.7)

14 (12.3)

12.8 (6.14)

0.312

Neutrophils, median (IQR)

10.7 (10.0)

10.8 (10.8)

10.2 (6.71)

0.257

Lymphocytes, median (IQR)

1.3 (1.53)

1.3 (1.49)

0.9 (0.98)

0.514

Platelets, mean ± SD

373.5 ± 139.5

292.4 ± 162.7

373.5 ± 139.5

0.073

CRP, median (IQR)

101.3 (158.4)

101.3 (152)

101.4 (236)

0.316

Albumin, mean ± SD

27.4 ± 7.9

27.9 ± 5.9

27.4 ± 7.9

0.792

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Table 3: Comparison of patient’s characteristics for patients with and without Pseudomonas infection (n=176)

Characteristics

Total (N=176)

Patients without Pseudomonas (n=160)

Patients with Pseudomonas (n=16)

P value

Presence of Sepsis, n (%)

83 (47.2)

76 (47.5)

7 (43.8)

0.774

Presence of Septic Shock, n (%)

46 (26.1)

44 (27.5)

2 (12.5)

0.193

Patient’s day (before antipseudomonal initiation), n (%)

1 (4)

1 (4)

4 (9)

0.001

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Forward Stepwise method, R2=0.293

Classification table = 91.9%

Hosmer and Lemeshow test: p <0.001

Omnibus Test of model coefficient: p=0.390

Variables

Simple Logistic Regression

Multiple Logistic Regression

OR (95% CI)

P value

Adj OR (95% CI)

P value

Presence of predisposing factors

2.814 (0.771-10.274)

0.117

​

​

Antibiotic Exposure within 30 days

3.729 (1.023-13.590)

0.046

​

​

Presence of Shock

0.377 (0.082-1.725)

0.208

​

​

PR upon ED admission

0.973 (0.947-0.999)

0.042

​

​

RR upon ED admission

0.903 (0.810-1.006)

0.064

​

​

PR upon antipseudomonal initiation

0.958 (0.932-0.984)

0.002

0.951 (0.916-0.987)

0.008

RR upon antipseudomonal initiation

0.893 (0.787-1.013)

0.079

​

​

Platelets upon antipseudomonal initiation

1.003 (1.000-1.006)

0.078

​

​

Patient days

1.113 (1.045-1.187)

0.001

1.102 (1.012-1.200)

0.025

Presence of SSTI

8.447 (1.561-45.711)

0.013

14.590 (2.394-88.933)

0.004

Presence of UTI

9.382 (1.159-75.966)

0.036

10.063 (1.106-91.584)

0.040

Table 4: Predictive factors for patients with pseudomonas infection in HTM

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Significant Clinical Predictors of Pseudomonas infection

Longer patient-days in ward

Adj OR = 1.102; 95% CI = 1.012-1.200; p = 0.025

Every 1-day increase in hospital stay, before β-APS is initiated, the chances of having pseudomonas infection will increase by 10.2%.

Slower pulse rate

Adj OR = 0.951; 95% CI = 0.916-0.987; p = 0.008

Presence of urinary tract infection (UTI)

Adj OR = 10.063; 95% CI = 1.106-91.584; p = 0.04

Presence of skin and soft tissue infection (SSTI)

Adj OR = 14.59; 95% CI = 2.394-88.933; p = 0.004

Probability of P. aeruginosa isolation increases linearly with the hospital stay since admission. [1]

[1] Daneman et al. (2012). J Clin Microbiol 50(8): 2695-2701.

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Significant Clinical Predictors of Pseudomonas infection

Longer patient-days in ward

Adj OR = 1.102; 95% CI = 1.012-1.200; p = 0.025

Presence of skin and soft tissue infection (SSTI)

Adj OR = 14.59; 95% CI = 2.394-88.933; p = 0.004

Presence of urinary tract infection (UTI)

Adj OR = 10.063; 95% CI = 1.106-91.584; p = 0.04

Slower pulse rate

Adj OR = 0.951; 95% CI = 0.916-0.987; p = 0.008

Patient with pseudomonas infection has slower pulse rate. Every 1 bpm PR reduce 5% predictive risk of pseudomonas infection.

There were no studies reporting on the association of Pseudomonas infection and slower pulse rate.

Relative bradycardia as a feature of specific disease is seen in typhoid fever, Legionnaire’s disease, and pneumonia caused by Chlamydia sp. [1]

[1] Ostergaard et al., J Infect. 1996 Nov;33(3):185-91

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Significant Clinical Predictors of Pseudomonas infection

Longer patient-days in ward

Adj OR = 1.102; 95% CI = 1.012-1.200; p = 0.025

Presence of skin and soft tissue infection (SSTI)

Adj OR = 14.59; 95% CI = 2.394-88.933; p = 0.004

Presence of urinary tract infection (UTI)

(Adj OR = 10.063; 95% CI = 1.106-91.584; p = 0.04)

In the presence of UTI, the chances of having Pseudomonas infection will increase by 10 folds.

Slower pulse rate

(Adj OR = 0.951; 95% CI = 0.916-0.987; p = 0.008

  • The third most common cause of catheter-associated urinary tract infections. [1]
  • E. coli and K. pneumoniae, and P. aeruginosa are the most common pathogens associated with UTIs. [2]

[1] Weiner et al., (2016) Infect Control Hosp Epidemiol, 37(11), 1288

[2] Newman et al., (2022) J Med Microbiol. Mar;71(3):001458

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Significant Clinical Predictors of Pseudomonas infection

Longer patient-days in ward

Adj OR = 1.102; 95% CI = 1.012-1.200; p = 0.025

Presence of skin and soft tissue infection (SSTI)

Adj OR = 14.59; 95% CI = 2.394-88.933; p = 0.004

Presence of urinary tract infection (UTI)

Adj OR = 10.063; 95% CI = 1.106-91.584; p = 0.04

Slower pulse rate

Adj OR = 0.951; 95% CI = 0.916-0.987; p = 0.008

  • Cutaneous manifestations of P. aeruginosa infection can be classified as either primary infection due to cutaneous inoculation, or those that are secondary to P. aeruginosa bacteremia. [1]
  • The predominant pathogens included Staphylococcus aureus (ranked 1st in all geographic regions), Pseudomonas aeruginosa, Escherichia coli, and Enterococcus spp. [2]

In the presence of SSTI, the chances of having Pseudomonas infection will increase by 14 folds.

[1] Wu e al. (2011). Am J Clin Dermatol 12, 157–169

[2] Moet et al.(2007) Diagn Microbiol Infect Dis. Jan;57(1):7-13

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31

Study done during covid

Wards are off limits to limit transmission of the disease, unable to enroll patient into the study which leads to small sample size.

Many disruption of antibiotic stock, may influence prescriber's decision on choice of β-APS

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32

​

STUDY POTENTIAL

​

Provide better understanding on the decision of prescribers in initiating antipseudomonal β-lactam antibiotics - optimal usage of antibiotics

​

Provide knowledge on risk factors of Pseudomonas infections - stratify patients into risk groups

​

STUDY LIMITATION

​

Insufficient sample size

​

Findings of this study may not be applicable to other facilities

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33

  • In this study, among 176 patients, 91.5% patients were treated empirically with antipseudomonal β-lactam. The most commonly prescribed B-APS was Piperacillin/Tazobactam.

​

  • Pseudomonas aeruginosa being isolated in was only 16 (9.1%).

​

  • Slower pulse rate, longer patient-days in ward, presence of UTI or SSTI are predictors of having pseudomonas infection.

​

  • Identification of risk factors - useful to stratify the patients into Pseudomonas infection risk groups.
    • High-risk patients can be initiated with prompt intensified treatment to reduce the undesirable outcomes; and
    • less aggressive treatment for low-risk patient

​

  • This study should be further carried out with larger sample size and extend to other facilities.

CONCLUSION

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ACKNOWLEDGEMENTS

34

  • ONG ANN GIE
  • NORANIZA BINTI MOHAMAD ZALIK
  • TUAN NOOR KHAIZURA BINTI TUAN RAHIM
  • AHMAD TARMIZI AHMAD ZAWAWI
  • WAN MUHAMMAD TAUFIK BIN WAN TAMAN
  • NUR HIDAYAH BINTI ZAKARIA
  • AHMAD FADZIL MUSLIM BIN MOHAMAD ZIN

PREVIOUS INVESTIGATORS

  • Pathology Department HTM
  • Records Unit HTM

SOURCE OF CLINICAL DATA

DIRECTOR GENERAL OF HEALTH MALAYSIA, DATUK DR MUHAMMAD RADZI ABU HASSAN

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