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R Clin Pharm 2024; 2(1): 18-26

Published online June 30, 2024 https://doi.org/10.59931/rcp.24.0003

Copyright © Asian Conference On Clinical Pharmacy.

Predictors of Unfavorable Outcomes among Patients with Newly Diagnosed Pulmonary Tuberculosis

Humayera Kabir Hana1 , Sania Siddiqui1 , Siti Maisharah Sheikh Ghadzi1 , Irfhan Ali Hyder Ali2 , Siew Chin Ong1 , Nur Hafzan Md Hanafiah1 , Sabariah Noor Harun1

1School of Pharmaceutical Sciences, Universiti Sains Malaysia, George Town, Malaysia
2Respiratory Department, Penang General Hospital, George Town, Malaysia

Correspondence to:Sabariah Noor Harun
E-mail sabariahnoor@usm.my
ORCID
https://orcid.org/0000-0001-8476-7425

Received: April 9, 2024; Revised: May 18, 2024; Accepted: May 24, 2024

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Background: Tuberculosis is a considerable hazard in Malaysia. According to the World Health Organization (WHO), the treatment success rate for newly diagnosed pulmonary tuberculosis patients (PTB) is lower than the global success target of 95%. Currently there are limited studies evaluating the predictors of treatment outcomes for newly diagnosed pulmonary tuberculosis patients. The current study aimed to determine the predictors for recently diagnosed PTB patients in Malaysia. The study also aimed to identify the effect of fixed-dose combinations (FDCs) and separate treatment regimens on PTB therapy.
Methods: In this retrospective cohort study, patients following up at the chest clinic at the Penang General Hospital were included. Convenient sampling was performed to collect data from patients who had been newly diagnosed from January 2014 to December 2017. SPSS version 23 was used for performing Cox regression analysis.
Results: Treating newly diagnosed PTB patients with FDCs in the intensive phase significantly reduced unfavorable outcomes (HR=0.266; 95% CI: 0.067–0.763). Age <60 years, drug abuse, and treatment interruption were the factors that considerably increased the hazards of unfavorable outcomes with HR=2.21 (95% CI: 1.05–4.65); 3.24 (95% CI: 1.24–8.49); and 2.702 (95% CI: 1.186–6.154) respectively.
Conclusion: The hazards of unfavorable outcomes in newly diagnosed PTB patients are significantly reduced by treating them with FDCs. However, treatment interruption adversely affects the outcome. Further studies investigating the effects of FDCs along with other factors influencing drug response relationships are paramount to improving treatment strategies.

KeywordsFDC; Tuberculosis; Clinical pharmacy; Risk factors; Unfavorable outcomes; Malaysia

Tuberculosis (TB) is caused by the bacterial pathogen Mycobacterium tuberculosis, which remains one of the deadliest infectious disease-causing billions of deaths in the past 200 years [1]. In comparison to the other infectious diseases, TB causes more deaths worldwide, affecting nearly 10.4 million new cases and close to 1.7 million deaths [2]. In Malaysia, the estimated rate of TB incidence was 92 cases per 100,000 population, while the mortality rate was 4 cases per 100,000 population per year [3]. TB mortality can be reduced through the development of effective vaccines, improved diagnostics, and by using novel shortened therapy regimens [4].

Published studies have found several factors to be associated with the unsuccessful treatment outcome among TB patients. The major contributing factors that were documented by published studies are HIV-positive status, male sex, ethnicity, low BMI, substance abuse, treatment duration, non-adherence, and at the top is drug resistance [5-9]. Drug resistance emerges when TB medicines are not used appropriately. The main cause of multi-drug resistance TB is the inclusion of an inadequate number of drugs susceptible to bacilli, inadequate dose or frequency given, low patient compliance and adherence to provided regimens [10]. Effective treatment for TB is the one that ensures rapid and lasting cures among TB patients and is considered a vital component of the TB control Program. The recommended oral drug therapy for TB includes isoniazid, rifampicin, pyrazinamide, and ethambutol. The standard short-course treatment for TB includes a rifampicin-based regimen, given daily or three times per week for a period of six months [11]. To prevent treatment failure, unfavorable outcomes and emergence of drug resistance and relapse of TB (screening of TB again, even after successful treatment) due to inappropriate drug intake, fixed dose combinations (FDCs) of drugs for TB treatment have been recommended internationally [12,13]. WHO and the International Union Against Tuberculosis and Lung Disease (IUATLD) together with their partners also recommend the use of fixed dose combination formulations of essential anti-tuberculosis drugs to ensure appropriate and adequate treatment [14,15].

TB disease is curable and preventable therefore, the treatment success rate is considered as pointer in gauging the success of the National TB program worldwide. The treatment success rate of TB in Malaysia just achieved the WHO target of 90% but still did not meet the local goal of 95% [16]. To date, very few studies have reported treatment outcomes of TB patients in Malaysia with high heterogeneity in results [17,18]. Neither of these studies has evaluated the predictors of favorable and unfavorable outcomes among newly diagnosed PTB patients. Furthermore, data is also lacking in evaluating the relationship between fixed dose combination therapy and separate-dose tablets on favorable and unfavorable outcomes among newly diagnosed PTB patients. The evaluation is necessary as it will help clinicians and healthcare practitioners in deciding the best treatment regimens that may lead to successful treatment outcomes among PTB patients, which increase patients’ compliance, reduce non-adherence to treatment medications, and will reduce the disease burden on the healthcare system. To fill the gaps in the available literature, the present study aimed to determine the predictors of newly diagnosed PTB patients in Malaysia, and to identify the effect of FDCS and separate regimens on PTB therapy.

Study Design and Participants

A retrospective cohort study was conducted at the Chest Clinic of Hospital Pulao Pinang Malaysia. A convenient sampling method was employed to collect the data of the patients diagnosed with PTB from January 2014 to December 2017.

Eligibility Criteria

The inclusion criteria for this study include new cases of PTB among adult patients aged ≥18 years, treated for FDC anti-TB regimen or separate tablet regimen. Participants who have been diagnosed with extra-pulmonary TB have been excluded from this study.

Data that have been collected include patients demographics (e.g., age, gender, ethnicity), PTB investigations such as chest X-ray findings, Acid fast bacilli (AFB) smear results, comorbidities, concurrent medications, clinical laboratory investigations at baseline (prior to anti-PTB initiation) and day 14th of PTB therapy (e.g., electrolyte level, fasting blood glucose, liver function test and complete blood count) and PTB treatment status (e.g., PTB regimen received during intensive and maintenance phase, doses, treatment duration, and interruption).

Operational Definitions

Unfavorable outcome: obtained variables such as death during the PTB therapy, treatment failure, and defaulted/loss to follow-up were grouped as ‘unfavorable’ outcome in this study.

Favorable outcome: obtained variables including cure from PTB, and treatment completed were combined as a ‘favorable’ outcome in the present study.

PTB treatment interruption was defined as any interruption of treatment for at least one day, but for less than 8 consecutive weeks due to adverse drug reactions to the anti-TB regimen.

Sample Size Estimation

According to findings from published studies, prevalence of PTB among Malaysian population is 19.6% [3]. Thus, the expected sample proportion of 0.19 was utilized in the estimation of sample size in this study. By using sample proportion (p) of 0.26, Z statistics 1.64 at a confidence level of 95%, at a precision (d) of 0.05, the minimum sample size required for this study is 241. The sample size increased to 273 patients after considering the 10% non-response rate.

n=N*X/X+N1,where,X=Zα/22*p*1pd2

Where,

n= sample size,

N= population size,

Z= Z statistic for a level of confidence,

P= sample proportion,

d= precision.

Ethical Considerations

As this study was a retrospective study which only involved the use of existing data, it imposed no harm on the patients as confidentiality was maintained (patient names and identification were not recorded). Inform consent from patients was not required for this study. Study was conducted in accordance with ethical guidelines and ethical approval was obtained from Medical Research and Ethics Committee (MREC), Ministry of Health Malaysia prior to initiation of the study (NMRR-18-95-39523 - [Research ID: 39523])

IBM SPSS for Windows version 22.0 (SPSS Inc., Chicago, III, USA) were used for analysis, p<0.05 was considered as statistically significant. The categorical variables were presented in percentage, while the values for continuous variables were expressed as the mean±standard deviation. A comparison between favorable and unfavorable outcome were held using the chi-square and Fisher’s exact tests for categorical variables, while an independent sample t-test/Mann–Whitney U test was used for continuous data. The significant variables obtained from the univariate analysis were then included into the multivariate Cox regression and association between exposure and outcomes was reported as hazard ratio (HR) with a 95% confidence interval (CI). To minimize bias from missing data, the pattern of missing values of independent variables was analyzed. Multiple imputations were used to handle variables with missing values above 5%. Missing values in clinical laboratory results were imputed from multiple imputation methods. Five imputations were used, and Rubin’s rules were implemented to combine the results.

Demographic Characteristics of Study Participants

A total of 273 participants were included in this study, the majority of them were male (n=207, 75.8%), and were Chinese (54.9%), smokers with no significant differences in proportion between the study groups (Table 1). When comparing age among the two groups, the majority of the PTB patients aged less than 60 years were found to have favorable outcomes as compared to subjects in the same age category with unfavorable outcomes (p=0.042). A significant difference was observed among drug abusers, where a high percentage of PTB drug abusers have a favorable outcome as compared to PTB patients with unfavorable outcomes (p=0.003). A total of 161 (59.0%) patients were cured while 22 (8.1%) patients died. Hypertension was the most prevalent concomitant disorder (75.1%) followed by diabetes (65.6%). Blood test results were obtained prior to treatment and fourteen days after treatment commencement.

Table 1 Characteristics of patients with newly diagnosed PTB included into the study and significant predictors of unfavourable PTB treatment outcome in univariate and multivariate cox regression model (n=273)

Variablesn (%)PTB favourable outcomes
n=241 (88.27%)
PTB unfavourable outcomes
n=32 (11.72%)
p-valuesHR (95%CI)p-values
Male207 (75.8)181 (75.1)26 (81.3)0.301
Age, mean (SD)49.43 (15.64)48.76 (15.1)54.4 (18.5)0.054
<60 years194 (71.1)176 (73.0)18 (56.3)0.042*2.21 (1.05–4.65)0.036**
≥60 years79 (28.9)65 (27.0)14 (43.8)
Ethnicity
Malay82 (30.0)77 (32.0)5 (15.6)0.166
Chinese150 (54.9)129 (53.5)21 (65.6)
Indian41 (15.0)35 (14.5)6 (18.8)
Body weight (kg), mean (SD)50.42 (19.61)50.69 (20.36)48.29 (12.66)0.514
Smoker183 (67.0)162 (67.2)21 (65.6)0.844
Alcohol intake146 (53.5)133 (55.2)13 (40.6)0.134
Drug abuser166 (60.8)139 (57.7)27 (84.4)0.003*3.24 (1.24–8.49)0.017**
Treatment duration (months), mean (SD)5.79 (2.29)6.29 (1.53)2.03 (3.4)<0.001
PTB treatment (intensive phase)
FDC-4 tablet regimen110 (80.9)105 (84.0)5 (45.5)0.007*0.226 (0.067–0.763)0.017**
Separate tablet regimen26 (19.1)20 (16.0)2 (54.5)
PTB treatment (maintenance phase)
FDC-2 tablet128 (46.9)122 (50.6)6 (18.8)0.400
Separate tablet regimen123 (45.1)119 (49.4)4 (12.5)
PTB treatment regimen
Received FDC regimen in both phases110 (40.3)105 (43.6)5 (15.6)0.008*0.278 (0.098–0.785)0.016**
Received separate regimen29 (10.6)23 (9.5)6 (18.8)1.056 (0.400–2.785)0.913
Received FDC regimen in either phases134 (49.1)113 (46.9)21 (65.6)Reference
PTB treatment outcome
Cure161 (59.0)---
Completed80 (29.3)---
Defaulted7 (2.6)---
Treatment failure3 (1.1)---
Died22 (8.1)---
Treatment interruption39 (14.3)29 (12.0)10 (31.3)0.012*2.702 (1.186–6.154)0.018**
Concomitant diseases
Diabetes179 (65.6)161 (66.8)18 (56.3)0.242
Hypertension205 (75.1)178 (73.9)27 (84.4)0.276
HIV16 (5.9)12 (5.0)4 (12.5)0.103
Received BCG vaccine155 (93.9)145 (93.5)10 (100)1.000
Positive smear AFB at baseline198 (72.5)173 (71.8)25 (78.1)0.532
Chest X-ray at baseline
Clear5 (1.8)5 (2.1)0 (0)0.284
Mild196 (71.8)169 (70.1)27 (84.4)
Moderate57 (20.9)54 (22.4)3 (9.4)
Advance15 (5.5)13 (5.4)2 (6.3)
Positive Xpert MTB/RIF molecular test at baseline31 (72.1)25 (67.6)6 (100)0.163
Blood test results prior to PTB therapy, mean (SD)
Sodium level (mmol/L)140.67 (10.60)135.88 (6.1)134.89 (6.8)0.428
Potassium level (mmol/L)3.63 (0.25)3.89 (0.59)4.02 (0.59)0.248
Chloride level (mmol/L)102.33 (7.02)97.27 (6.01)96.94 (7.47)0.773
Urea level (mmol/L)5.47 (1.72)4.41 (3.67)7.60 (6.63)<0.0011.042 (0.991–1.095)0.107
Serum creatinine (μmol/L)85.67 (16.50)83.98 (98.67)103.81 (84.75)0.279
Fasting blood sugar (mmol/L)7.67 (6.47)7.29 (4.32)6.83 (4.15)0.566
Albumin level (g/L)26.0 (1.73)28.73 (7.15)23.54 (7.81)<0.001*0.923 (0.877–0.971)0.002**
Bilirubin level (μmol/L)13.67 (11.59)12.65 (10.29)15.56 (16.5)0.168
ALT level (U/L)50.00 (25.46)24.45 (27.8)29.41 (36.59)0.353
ALP level (U/L)237.00 (243.48)102.72 (47.25)166.06 (342.1)0.001*1.002 (1.000–1.003)0.008**
WBC level ×109/L12.17 (4.53)9.91 (5.09)8.59 (4.74)0.170
RBC level ×1012/L5.07 (0.29)4.56 (0.82)4.13 (0.68)0.0160.822 (0.437–1.547)0.541
HgB level (g/dL)13.37 (1.38)12.01 (2.19)10.88 (2.09)0.0060.921 (0.720–1.179)0.494
HCT level %41.10 (3.26)36.75 (±6.24)33.27 (±5.09)0.0120.987 (0.900–1.064)0.606
Platelet level ×109/L152.0 (84.33)373.81 (±151.18)306.03 (±154.05)0.0180.997 (0.994–1.000)0.061
Neutrophil level %80.73 (13.02)70.41 (±13.14)75.52 (±11.88)0.0381.006 (0.966–1.048)0.759
Eosinophil level ×109/L0.83 (0.50)2.36 (±4.29)1.36 (±2.01)0.332
Lymphocyte level ×109/L10.57 (8.33)18.80 (±9.65)11.93 (±8.58)0.0050.958 (0.895–1.025)0.214
Blood test result at day 14 of therapy, mean (SD)
Albumin level (g/L)29.5 (7.13)29.86 (±6.93)24.81 (±8.34)<0.0010.979 (0.894–1.072)0.624
Bilirubin level (μmol/L)8.72 (13.99)8.48 (±13.80)15.22 (±23.69)0.0201.005 (0.990–1.021)0.490
ALT level (U/L)23.13 (20.57)22.58 (±20.27)29.16 (±23.4)0.183
ALP level (U/L)99.58 (39.14)96.19 (±32.02)111.28 (±64.37)0.0321.004 (0.993–1.014)0.489

*Fisher’s exact test, **significant value in final model.

FDC=fixed-dose combination, FDC-4=fixed-dose combination consists of four drugs, SD=standard deviation, FDC-2=fixed-dose combination consists of two drugs, FDC-3=fixed-dose combination consists of three drugs, BCG=Bacillus Calmette–Guérin vaccine, ALT=alanine aminotransferase, ALP=alkaline phosphatase, WBC=white blood cells, RBC=red blood cells, HgB=haemoglobin, HCT=haematocrit. Highlighted in grey=significant variables in univariate analysis, Highlighted in blue=significant variables in the final multivariate cox regression analysis.


Associated Predictors of Unfavorable Outcomes

Age has been observed as one of the factors contributing to the unfavorable outcomes in the present study (HR=2.21; 95% CI 1.05–4.65; p=0.036) which indicate aged ≥60 years have 2.21 times the risk of unfavorable outcomes compared to those <60 years. It has also been observed that PTB patients who are drug abusers are at higher risk of having unfavorable outcomes (HR=3.24; 95% CI 1.24–8.49; p=0.017). Furthermore, patients who received FDC regimen in both phases are less likely to have unfavorable outcomes than those receiving a separate regimen or receiving FDC regimen in either phase of treatment (HR=0.278; 95% CI 0.098–0.785; p=0.016) (Fig. 1). Looking at individual phases, the use of FDC in the intensive phase significantly reduces the hazard of unfavorable outcomes by 77.4% as compared to the use of a separate regimen in the maintenance phase. A significant association has been observed between treatment interruption and unfavorable outcomes with a p-value=0.018. Similarly, based on blood test results prior to PTB therapy, albumin level has been found to be associated with unfavorable outcomes (HR=0.923; 95% CI 0.877–0.971; p=0.002); for every 1 g/L increase in albumin, the risk of unfavorable outcomes decreases by approximately 7.7% as shown in Table 1.

Figure 1. Significant predictors of unfavorable outcomes in newly diagnosed pulmonary tuberculosis (PTB) patients. This bar chart presents the hazard ratios (HRs) for key variables influencing outcomes in newly diagnosed PTB patients. Factors associated with an increased risk of unfavorable outcomes include being under 60 years of age (HR=2.21), drug abuse (HR=3.24), and treatment interruption (HR=2.702). Conversely, using fixed-dose combination (FDC) therapy during the intensive phase (HR=0.226) and throughout both treatment phases (HR=0.278) were protective factors. Albumin level (HR=0.923) demonstrated a minimal protective association. Hazard ratios above 1 indicate a higher risk of unfavorable outcomes, while those below 1 suggest protective effects.

Present study has evaluated the effect of contributing factors that affect treatment outcomes among newly diagnosed pulmonary tuberculosis (PTB) patients. Several contributing factors were observed which has impact on the outcomes in patients under treatment. Age has been observed as one of the factors contributing to the unfavourable outcomes in the present study (HR=2.21; 95% CI 1.05-4.65; p=0.036). The findings were in line with the published study conducted among the Ethiopian population where age ≥55 years was significantly associated with poor treatment outcomes among PTB patients (AOR 1.44, 95% CI 1.12–1.86) [19]. On the other hand, another research study conducted in the same region showed that PTB patients who are in the range of 15–24 years old have better treatment success rate as compared to the older age group [20]. Unfavourable outcomes with the increase in age could be attributable to the increased risk of low immunity that makes them susceptible to more infections, comorbidities, physiological deterioration associated with age and the difficulties to access several healthcare options with increasing age [21].

Treatment interruption is one of the major causes of treatment failure and recurrence of tuberculosis in the present study. It can be associated with non-adherence to the PTB treatment or with a history of prior PTB treatment, although the present study has no patient with prior PTB treatment as all the cases were newly diagnosed. Similar findings were observed from published studies where non-adherence to TB regimen observed as contributing factor to poor outcome among PTB patients. The likely reason for this effect can be explained by multiple factors including the patient’s forgetfulness, lack of knowledge and incorrect perception of TB, the presence of more than one co-morbidity, and long distance to health institutions for receiving treatment care [22]. According to a published study among TB patients who travelled more than 10 kilometres to reach the health care facility were 6.55 times more likely to have poor treatment outcome compared to those who travelled less [23]. Likewise, a Japanese study conducted among PTB patients revealed that the non-acceptance, frustration, and anxiety among PTB patient is the major contributing factor to non-adherence [24]. A study conducted among Indonesian TB patients postulated that the lack of knowledge and incorrect perception of TB before therapy were associated with low treatment compliance among TB patients [25]. Polypharmacy and complexity of treatment regimen are known to be the major determinants of poor medication compliance. A survey conducted among Korean TB patients reported that approximately 45% of TB patients were non-adherent to TB medications where multimorbidity, and polypharmacy was the major contributing factor to non-adherence [26]. A survey conducted on patient information and education by the National Council proved that one-third of patients receive at least 2 prescriptions and 10% of patients receive 4 or more prescriptions when they visit a primary care physician [27]. Efforts have been made to simplify the drug regimen. Interventions aimed at simplifying the drug regimen for patients (e.g., daily dosing as opposed to twice daily dosing) have been shown to improve patients’ compliance in studies [28]. Similarly, incomplete treatment may be associated with the development of adverse events secondary to anti-TB drugs. Drug intolerance secondary to adverse effects was one of the leading factors for not completing the course of therapy and hence carries a broader risk of unfavorable outcomes as well as drug resistance [29].

Low serum albumin levels may reflect poor nutritional status and are associated with an increased risk of in-hospital mortality in patients suffering from tuberculosis, a chronic disease [30]. Present study observed a strong association of serum albumin with the treatment outcomes of PTB. An earlier study conducted in Malaysia has observed that low serum albumin is a predictor of unfavorable outcomes associated with malnutrition in patients with tuberculosis [30]. Similarly, a study conducted in Brazil has observed that low serum albumin was strongly and independently associated with in-hospital death of patients under PTB treatment [31].

Alcohol and drug abuse have been found to be one of the predominant factors significantly associated with non-compliance or non-adherence to medicine in multiple studies. Drug abuse has been significantly associated with unfavorable outcomes in the present study. Similar findings were reported by a published study where drug addiction was found as a major concern in patients treated for tuberculosis (AOR: 7.3, CI [2.89–18.46]). Drug abuse with another chronic disease is very complex and intensive, hence requiring prompt intervention to reduce the misuse of alcohol and substance addiction among these patients [21]. It is suggested that these patients can be effectively managed through behavioral contracting, skill training, brief interventions, and also through pharmacotherapy [17].

Low albumin may affect the pharmacokinetics of highly protein-bound antituberculosis especially isoniazid to some extent [32]. The effect of a high concentration of unbound drugs on the adverse effect development may need to be investigated. Moreover, future studies elucidating the dose-response relationship of FDC anti-tuberculosis and factors influencing the variabilities in blood concentration are required to enhance the treatment outcome.

Furthermore, the present study has observed that FDC carries higher chances of favorable outcomes in the intensive phase. However, treatment interruption may explain the non-significant effect of FDC in the maintenance phase on favorable outcomes. FDC has shown better results on the patient’s compliance and adherence to published studies [33]. A multicenter study conducted by International Union against Tuberculosis and lung disease, has concluded that the efficacy and safety results of FDC is the same as for the separate dose, however, acceptance of FDC among patients is significantly better than the separate doses [34]. It is worth mentioning that improved patient acceptance is one of the determining factors in the success of TB treatment and hence FDC can be considered superior although the safety and efficacy remain the same. A prospective study conducted in Indonesia, support the results of the current study and shows that although both the regimens have the same efficacy and safety profile, still the patients’ and practitioners are concerned about the number of medications taken daily in separate dose regimen that disrupt the patients’ daily activities and hence affect the treatment outcome. Furthermore, the study has also observed that gastrointestinal and muscle-joint complaints are comparatively less in the case of FDC than in separate doses [24]. Most of these studies have focused on the intensive phase of therapy for FDC regimens, yet our study shows better effects in both phases. As observed in a meta-analysis focusing on the fixed-dose combination, FDC is non-inferior to the separate dose treatment with less adverse effects and better compliance rates, hence further investigative studies are of prime importance to obtain conclusive evidence in the replacement of separate dose regimens with FDC regimen [35].

The present study was retrospective in nature and the data was obtained from the database of one hospital. The patients included in the study were not assessed directly and hence it may lead to the unavailability of certain data related to the adverse effects. The number of patients with comorbidities was comparatively less which may not provide proper explanation of the impacts of these comorbid conditions on PTB therapy. The study was focused on the PTB and hence cannot be generalized to other forms of TB.

Our retrospective investigation has observed multiple predictors which can interact with treatment outcomes i.e., age, drug abusers, FDC and treatment interruptions. All these factors need to be addressed properly for achieving favorable results while treating PTB patients. Further studies determine the effect of FDC and other factors on drug response relationship is paramount to improve the treatment strategies.

We would like to thank the Director General of Health Malaysia for his permission to publish this article.

No potential conflict of interest relevant to this article was reported.

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Article

Original Article

R Clin Pharm 2024; 2(1): 18-26

Published online June 30, 2024 https://doi.org/10.59931/rcp.24.0003

Copyright © Asian Conference On Clinical Pharmacy.

Predictors of Unfavorable Outcomes among Patients with Newly Diagnosed Pulmonary Tuberculosis

Humayera Kabir Hana1 , Sania Siddiqui1 , Siti Maisharah Sheikh Ghadzi1 , Irfhan Ali Hyder Ali2 , Siew Chin Ong1 , Nur Hafzan Md Hanafiah1 , Sabariah Noor Harun1

1School of Pharmaceutical Sciences, Universiti Sains Malaysia, George Town, Malaysia
2Respiratory Department, Penang General Hospital, George Town, Malaysia

Correspondence to:Sabariah Noor Harun
E-mail sabariahnoor@usm.my
ORCID
https://orcid.org/0000-0001-8476-7425

Received: April 9, 2024; Revised: May 18, 2024; Accepted: May 24, 2024

This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/4.0/), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Background: Tuberculosis is a considerable hazard in Malaysia. According to the World Health Organization (WHO), the treatment success rate for newly diagnosed pulmonary tuberculosis patients (PTB) is lower than the global success target of 95%. Currently there are limited studies evaluating the predictors of treatment outcomes for newly diagnosed pulmonary tuberculosis patients. The current study aimed to determine the predictors for recently diagnosed PTB patients in Malaysia. The study also aimed to identify the effect of fixed-dose combinations (FDCs) and separate treatment regimens on PTB therapy.
Methods: In this retrospective cohort study, patients following up at the chest clinic at the Penang General Hospital were included. Convenient sampling was performed to collect data from patients who had been newly diagnosed from January 2014 to December 2017. SPSS version 23 was used for performing Cox regression analysis.
Results: Treating newly diagnosed PTB patients with FDCs in the intensive phase significantly reduced unfavorable outcomes (HR=0.266; 95% CI: 0.067–0.763). Age <60 years, drug abuse, and treatment interruption were the factors that considerably increased the hazards of unfavorable outcomes with HR=2.21 (95% CI: 1.05–4.65); 3.24 (95% CI: 1.24–8.49); and 2.702 (95% CI: 1.186–6.154) respectively.
Conclusion: The hazards of unfavorable outcomes in newly diagnosed PTB patients are significantly reduced by treating them with FDCs. However, treatment interruption adversely affects the outcome. Further studies investigating the effects of FDCs along with other factors influencing drug response relationships are paramount to improving treatment strategies.

Keywords: FDC, Tuberculosis, Clinical pharmacy, Risk factors, Unfavorable outcomes, Malaysia

Body

Tuberculosis (TB) is caused by the bacterial pathogen Mycobacterium tuberculosis, which remains one of the deadliest infectious disease-causing billions of deaths in the past 200 years [1]. In comparison to the other infectious diseases, TB causes more deaths worldwide, affecting nearly 10.4 million new cases and close to 1.7 million deaths [2]. In Malaysia, the estimated rate of TB incidence was 92 cases per 100,000 population, while the mortality rate was 4 cases per 100,000 population per year [3]. TB mortality can be reduced through the development of effective vaccines, improved diagnostics, and by using novel shortened therapy regimens [4].

Published studies have found several factors to be associated with the unsuccessful treatment outcome among TB patients. The major contributing factors that were documented by published studies are HIV-positive status, male sex, ethnicity, low BMI, substance abuse, treatment duration, non-adherence, and at the top is drug resistance [5-9]. Drug resistance emerges when TB medicines are not used appropriately. The main cause of multi-drug resistance TB is the inclusion of an inadequate number of drugs susceptible to bacilli, inadequate dose or frequency given, low patient compliance and adherence to provided regimens [10]. Effective treatment for TB is the one that ensures rapid and lasting cures among TB patients and is considered a vital component of the TB control Program. The recommended oral drug therapy for TB includes isoniazid, rifampicin, pyrazinamide, and ethambutol. The standard short-course treatment for TB includes a rifampicin-based regimen, given daily or three times per week for a period of six months [11]. To prevent treatment failure, unfavorable outcomes and emergence of drug resistance and relapse of TB (screening of TB again, even after successful treatment) due to inappropriate drug intake, fixed dose combinations (FDCs) of drugs for TB treatment have been recommended internationally [12,13]. WHO and the International Union Against Tuberculosis and Lung Disease (IUATLD) together with their partners also recommend the use of fixed dose combination formulations of essential anti-tuberculosis drugs to ensure appropriate and adequate treatment [14,15].

TB disease is curable and preventable therefore, the treatment success rate is considered as pointer in gauging the success of the National TB program worldwide. The treatment success rate of TB in Malaysia just achieved the WHO target of 90% but still did not meet the local goal of 95% [16]. To date, very few studies have reported treatment outcomes of TB patients in Malaysia with high heterogeneity in results [17,18]. Neither of these studies has evaluated the predictors of favorable and unfavorable outcomes among newly diagnosed PTB patients. Furthermore, data is also lacking in evaluating the relationship between fixed dose combination therapy and separate-dose tablets on favorable and unfavorable outcomes among newly diagnosed PTB patients. The evaluation is necessary as it will help clinicians and healthcare practitioners in deciding the best treatment regimens that may lead to successful treatment outcomes among PTB patients, which increase patients’ compliance, reduce non-adherence to treatment medications, and will reduce the disease burden on the healthcare system. To fill the gaps in the available literature, the present study aimed to determine the predictors of newly diagnosed PTB patients in Malaysia, and to identify the effect of FDCS and separate regimens on PTB therapy.

METHODS

Study Design and Participants

A retrospective cohort study was conducted at the Chest Clinic of Hospital Pulao Pinang Malaysia. A convenient sampling method was employed to collect the data of the patients diagnosed with PTB from January 2014 to December 2017.

Eligibility Criteria

The inclusion criteria for this study include new cases of PTB among adult patients aged ≥18 years, treated for FDC anti-TB regimen or separate tablet regimen. Participants who have been diagnosed with extra-pulmonary TB have been excluded from this study.

Data that have been collected include patients demographics (e.g., age, gender, ethnicity), PTB investigations such as chest X-ray findings, Acid fast bacilli (AFB) smear results, comorbidities, concurrent medications, clinical laboratory investigations at baseline (prior to anti-PTB initiation) and day 14th of PTB therapy (e.g., electrolyte level, fasting blood glucose, liver function test and complete blood count) and PTB treatment status (e.g., PTB regimen received during intensive and maintenance phase, doses, treatment duration, and interruption).

Operational Definitions

Unfavorable outcome: obtained variables such as death during the PTB therapy, treatment failure, and defaulted/loss to follow-up were grouped as ‘unfavorable’ outcome in this study.

Favorable outcome: obtained variables including cure from PTB, and treatment completed were combined as a ‘favorable’ outcome in the present study.

PTB treatment interruption was defined as any interruption of treatment for at least one day, but for less than 8 consecutive weeks due to adverse drug reactions to the anti-TB regimen.

Sample Size Estimation

According to findings from published studies, prevalence of PTB among Malaysian population is 19.6% [3]. Thus, the expected sample proportion of 0.19 was utilized in the estimation of sample size in this study. By using sample proportion (p) of 0.26, Z statistics 1.64 at a confidence level of 95%, at a precision (d) of 0.05, the minimum sample size required for this study is 241. The sample size increased to 273 patients after considering the 10% non-response rate.

n=N*X/X+N1,where,X=Zα/22*p*1pd2

Where,

n= sample size,

N= population size,

Z= Z statistic for a level of confidence,

P= sample proportion,

d= precision.

Ethical Considerations

As this study was a retrospective study which only involved the use of existing data, it imposed no harm on the patients as confidentiality was maintained (patient names and identification were not recorded). Inform consent from patients was not required for this study. Study was conducted in accordance with ethical guidelines and ethical approval was obtained from Medical Research and Ethics Committee (MREC), Ministry of Health Malaysia prior to initiation of the study (NMRR-18-95-39523 - [Research ID: 39523])

STATISTICAL ANALYSIS

IBM SPSS for Windows version 22.0 (SPSS Inc., Chicago, III, USA) were used for analysis, p<0.05 was considered as statistically significant. The categorical variables were presented in percentage, while the values for continuous variables were expressed as the mean±standard deviation. A comparison between favorable and unfavorable outcome were held using the chi-square and Fisher’s exact tests for categorical variables, while an independent sample t-test/Mann–Whitney U test was used for continuous data. The significant variables obtained from the univariate analysis were then included into the multivariate Cox regression and association between exposure and outcomes was reported as hazard ratio (HR) with a 95% confidence interval (CI). To minimize bias from missing data, the pattern of missing values of independent variables was analyzed. Multiple imputations were used to handle variables with missing values above 5%. Missing values in clinical laboratory results were imputed from multiple imputation methods. Five imputations were used, and Rubin’s rules were implemented to combine the results.

RESULTS

Demographic Characteristics of Study Participants

A total of 273 participants were included in this study, the majority of them were male (n=207, 75.8%), and were Chinese (54.9%), smokers with no significant differences in proportion between the study groups (Table 1). When comparing age among the two groups, the majority of the PTB patients aged less than 60 years were found to have favorable outcomes as compared to subjects in the same age category with unfavorable outcomes (p=0.042). A significant difference was observed among drug abusers, where a high percentage of PTB drug abusers have a favorable outcome as compared to PTB patients with unfavorable outcomes (p=0.003). A total of 161 (59.0%) patients were cured while 22 (8.1%) patients died. Hypertension was the most prevalent concomitant disorder (75.1%) followed by diabetes (65.6%). Blood test results were obtained prior to treatment and fourteen days after treatment commencement.

Table 1 . Characteristics of patients with newly diagnosed PTB included into the study and significant predictors of unfavourable PTB treatment outcome in univariate and multivariate cox regression model (n=273).

Variablesn (%)PTB favourable outcomes
n=241 (88.27%)
PTB unfavourable outcomes
n=32 (11.72%)
p-valuesHR (95%CI)p-values
Male207 (75.8)181 (75.1)26 (81.3)0.301
Age, mean (SD)49.43 (15.64)48.76 (15.1)54.4 (18.5)0.054
<60 years194 (71.1)176 (73.0)18 (56.3)0.042*2.21 (1.05–4.65)0.036**
≥60 years79 (28.9)65 (27.0)14 (43.8)
Ethnicity
Malay82 (30.0)77 (32.0)5 (15.6)0.166
Chinese150 (54.9)129 (53.5)21 (65.6)
Indian41 (15.0)35 (14.5)6 (18.8)
Body weight (kg), mean (SD)50.42 (19.61)50.69 (20.36)48.29 (12.66)0.514
Smoker183 (67.0)162 (67.2)21 (65.6)0.844
Alcohol intake146 (53.5)133 (55.2)13 (40.6)0.134
Drug abuser166 (60.8)139 (57.7)27 (84.4)0.003*3.24 (1.24–8.49)0.017**
Treatment duration (months), mean (SD)5.79 (2.29)6.29 (1.53)2.03 (3.4)<0.001
PTB treatment (intensive phase)
FDC-4 tablet regimen110 (80.9)105 (84.0)5 (45.5)0.007*0.226 (0.067–0.763)0.017**
Separate tablet regimen26 (19.1)20 (16.0)2 (54.5)
PTB treatment (maintenance phase)
FDC-2 tablet128 (46.9)122 (50.6)6 (18.8)0.400
Separate tablet regimen123 (45.1)119 (49.4)4 (12.5)
PTB treatment regimen
Received FDC regimen in both phases110 (40.3)105 (43.6)5 (15.6)0.008*0.278 (0.098–0.785)0.016**
Received separate regimen29 (10.6)23 (9.5)6 (18.8)1.056 (0.400–2.785)0.913
Received FDC regimen in either phases134 (49.1)113 (46.9)21 (65.6)Reference
PTB treatment outcome
Cure161 (59.0)---
Completed80 (29.3)---
Defaulted7 (2.6)---
Treatment failure3 (1.1)---
Died22 (8.1)---
Treatment interruption39 (14.3)29 (12.0)10 (31.3)0.012*2.702 (1.186–6.154)0.018**
Concomitant diseases
Diabetes179 (65.6)161 (66.8)18 (56.3)0.242
Hypertension205 (75.1)178 (73.9)27 (84.4)0.276
HIV16 (5.9)12 (5.0)4 (12.5)0.103
Received BCG vaccine155 (93.9)145 (93.5)10 (100)1.000
Positive smear AFB at baseline198 (72.5)173 (71.8)25 (78.1)0.532
Chest X-ray at baseline
Clear5 (1.8)5 (2.1)0 (0)0.284
Mild196 (71.8)169 (70.1)27 (84.4)
Moderate57 (20.9)54 (22.4)3 (9.4)
Advance15 (5.5)13 (5.4)2 (6.3)
Positive Xpert MTB/RIF molecular test at baseline31 (72.1)25 (67.6)6 (100)0.163
Blood test results prior to PTB therapy, mean (SD)
Sodium level (mmol/L)140.67 (10.60)135.88 (6.1)134.89 (6.8)0.428
Potassium level (mmol/L)3.63 (0.25)3.89 (0.59)4.02 (0.59)0.248
Chloride level (mmol/L)102.33 (7.02)97.27 (6.01)96.94 (7.47)0.773
Urea level (mmol/L)5.47 (1.72)4.41 (3.67)7.60 (6.63)<0.0011.042 (0.991–1.095)0.107
Serum creatinine (μmol/L)85.67 (16.50)83.98 (98.67)103.81 (84.75)0.279
Fasting blood sugar (mmol/L)7.67 (6.47)7.29 (4.32)6.83 (4.15)0.566
Albumin level (g/L)26.0 (1.73)28.73 (7.15)23.54 (7.81)<0.001*0.923 (0.877–0.971)0.002**
Bilirubin level (μmol/L)13.67 (11.59)12.65 (10.29)15.56 (16.5)0.168
ALT level (U/L)50.00 (25.46)24.45 (27.8)29.41 (36.59)0.353
ALP level (U/L)237.00 (243.48)102.72 (47.25)166.06 (342.1)0.001*1.002 (1.000–1.003)0.008**
WBC level ×109/L12.17 (4.53)9.91 (5.09)8.59 (4.74)0.170
RBC level ×1012/L5.07 (0.29)4.56 (0.82)4.13 (0.68)0.0160.822 (0.437–1.547)0.541
HgB level (g/dL)13.37 (1.38)12.01 (2.19)10.88 (2.09)0.0060.921 (0.720–1.179)0.494
HCT level %41.10 (3.26)36.75 (±6.24)33.27 (±5.09)0.0120.987 (0.900–1.064)0.606
Platelet level ×109/L152.0 (84.33)373.81 (±151.18)306.03 (±154.05)0.0180.997 (0.994–1.000)0.061
Neutrophil level %80.73 (13.02)70.41 (±13.14)75.52 (±11.88)0.0381.006 (0.966–1.048)0.759
Eosinophil level ×109/L0.83 (0.50)2.36 (±4.29)1.36 (±2.01)0.332
Lymphocyte level ×109/L10.57 (8.33)18.80 (±9.65)11.93 (±8.58)0.0050.958 (0.895–1.025)0.214
Blood test result at day 14 of therapy, mean (SD)
Albumin level (g/L)29.5 (7.13)29.86 (±6.93)24.81 (±8.34)<0.0010.979 (0.894–1.072)0.624
Bilirubin level (μmol/L)8.72 (13.99)8.48 (±13.80)15.22 (±23.69)0.0201.005 (0.990–1.021)0.490
ALT level (U/L)23.13 (20.57)22.58 (±20.27)29.16 (±23.4)0.183
ALP level (U/L)99.58 (39.14)96.19 (±32.02)111.28 (±64.37)0.0321.004 (0.993–1.014)0.489

*Fisher’s exact test, **significant value in final model..

FDC=fixed-dose combination, FDC-4=fixed-dose combination consists of four drugs, SD=standard deviation, FDC-2=fixed-dose combination consists of two drugs, FDC-3=fixed-dose combination consists of three drugs, BCG=Bacillus Calmette–Guérin vaccine, ALT=alanine aminotransferase, ALP=alkaline phosphatase, WBC=white blood cells, RBC=red blood cells, HgB=haemoglobin, HCT=haematocrit. Highlighted in grey=significant variables in univariate analysis, Highlighted in blue=significant variables in the final multivariate cox regression analysis..



Associated Predictors of Unfavorable Outcomes

Age has been observed as one of the factors contributing to the unfavorable outcomes in the present study (HR=2.21; 95% CI 1.05–4.65; p=0.036) which indicate aged ≥60 years have 2.21 times the risk of unfavorable outcomes compared to those <60 years. It has also been observed that PTB patients who are drug abusers are at higher risk of having unfavorable outcomes (HR=3.24; 95% CI 1.24–8.49; p=0.017). Furthermore, patients who received FDC regimen in both phases are less likely to have unfavorable outcomes than those receiving a separate regimen or receiving FDC regimen in either phase of treatment (HR=0.278; 95% CI 0.098–0.785; p=0.016) (Fig. 1). Looking at individual phases, the use of FDC in the intensive phase significantly reduces the hazard of unfavorable outcomes by 77.4% as compared to the use of a separate regimen in the maintenance phase. A significant association has been observed between treatment interruption and unfavorable outcomes with a p-value=0.018. Similarly, based on blood test results prior to PTB therapy, albumin level has been found to be associated with unfavorable outcomes (HR=0.923; 95% CI 0.877–0.971; p=0.002); for every 1 g/L increase in albumin, the risk of unfavorable outcomes decreases by approximately 7.7% as shown in Table 1.

Figure 1. Significant predictors of unfavorable outcomes in newly diagnosed pulmonary tuberculosis (PTB) patients. This bar chart presents the hazard ratios (HRs) for key variables influencing outcomes in newly diagnosed PTB patients. Factors associated with an increased risk of unfavorable outcomes include being under 60 years of age (HR=2.21), drug abuse (HR=3.24), and treatment interruption (HR=2.702). Conversely, using fixed-dose combination (FDC) therapy during the intensive phase (HR=0.226) and throughout both treatment phases (HR=0.278) were protective factors. Albumin level (HR=0.923) demonstrated a minimal protective association. Hazard ratios above 1 indicate a higher risk of unfavorable outcomes, while those below 1 suggest protective effects.

DISCUSSION

Present study has evaluated the effect of contributing factors that affect treatment outcomes among newly diagnosed pulmonary tuberculosis (PTB) patients. Several contributing factors were observed which has impact on the outcomes in patients under treatment. Age has been observed as one of the factors contributing to the unfavourable outcomes in the present study (HR=2.21; 95% CI 1.05-4.65; p=0.036). The findings were in line with the published study conducted among the Ethiopian population where age ≥55 years was significantly associated with poor treatment outcomes among PTB patients (AOR 1.44, 95% CI 1.12–1.86) [19]. On the other hand, another research study conducted in the same region showed that PTB patients who are in the range of 15–24 years old have better treatment success rate as compared to the older age group [20]. Unfavourable outcomes with the increase in age could be attributable to the increased risk of low immunity that makes them susceptible to more infections, comorbidities, physiological deterioration associated with age and the difficulties to access several healthcare options with increasing age [21].

Treatment interruption is one of the major causes of treatment failure and recurrence of tuberculosis in the present study. It can be associated with non-adherence to the PTB treatment or with a history of prior PTB treatment, although the present study has no patient with prior PTB treatment as all the cases were newly diagnosed. Similar findings were observed from published studies where non-adherence to TB regimen observed as contributing factor to poor outcome among PTB patients. The likely reason for this effect can be explained by multiple factors including the patient’s forgetfulness, lack of knowledge and incorrect perception of TB, the presence of more than one co-morbidity, and long distance to health institutions for receiving treatment care [22]. According to a published study among TB patients who travelled more than 10 kilometres to reach the health care facility were 6.55 times more likely to have poor treatment outcome compared to those who travelled less [23]. Likewise, a Japanese study conducted among PTB patients revealed that the non-acceptance, frustration, and anxiety among PTB patient is the major contributing factor to non-adherence [24]. A study conducted among Indonesian TB patients postulated that the lack of knowledge and incorrect perception of TB before therapy were associated with low treatment compliance among TB patients [25]. Polypharmacy and complexity of treatment regimen are known to be the major determinants of poor medication compliance. A survey conducted among Korean TB patients reported that approximately 45% of TB patients were non-adherent to TB medications where multimorbidity, and polypharmacy was the major contributing factor to non-adherence [26]. A survey conducted on patient information and education by the National Council proved that one-third of patients receive at least 2 prescriptions and 10% of patients receive 4 or more prescriptions when they visit a primary care physician [27]. Efforts have been made to simplify the drug regimen. Interventions aimed at simplifying the drug regimen for patients (e.g., daily dosing as opposed to twice daily dosing) have been shown to improve patients’ compliance in studies [28]. Similarly, incomplete treatment may be associated with the development of adverse events secondary to anti-TB drugs. Drug intolerance secondary to adverse effects was one of the leading factors for not completing the course of therapy and hence carries a broader risk of unfavorable outcomes as well as drug resistance [29].

Low serum albumin levels may reflect poor nutritional status and are associated with an increased risk of in-hospital mortality in patients suffering from tuberculosis, a chronic disease [30]. Present study observed a strong association of serum albumin with the treatment outcomes of PTB. An earlier study conducted in Malaysia has observed that low serum albumin is a predictor of unfavorable outcomes associated with malnutrition in patients with tuberculosis [30]. Similarly, a study conducted in Brazil has observed that low serum albumin was strongly and independently associated with in-hospital death of patients under PTB treatment [31].

Alcohol and drug abuse have been found to be one of the predominant factors significantly associated with non-compliance or non-adherence to medicine in multiple studies. Drug abuse has been significantly associated with unfavorable outcomes in the present study. Similar findings were reported by a published study where drug addiction was found as a major concern in patients treated for tuberculosis (AOR: 7.3, CI [2.89–18.46]). Drug abuse with another chronic disease is very complex and intensive, hence requiring prompt intervention to reduce the misuse of alcohol and substance addiction among these patients [21]. It is suggested that these patients can be effectively managed through behavioral contracting, skill training, brief interventions, and also through pharmacotherapy [17].

Low albumin may affect the pharmacokinetics of highly protein-bound antituberculosis especially isoniazid to some extent [32]. The effect of a high concentration of unbound drugs on the adverse effect development may need to be investigated. Moreover, future studies elucidating the dose-response relationship of FDC anti-tuberculosis and factors influencing the variabilities in blood concentration are required to enhance the treatment outcome.

Furthermore, the present study has observed that FDC carries higher chances of favorable outcomes in the intensive phase. However, treatment interruption may explain the non-significant effect of FDC in the maintenance phase on favorable outcomes. FDC has shown better results on the patient’s compliance and adherence to published studies [33]. A multicenter study conducted by International Union against Tuberculosis and lung disease, has concluded that the efficacy and safety results of FDC is the same as for the separate dose, however, acceptance of FDC among patients is significantly better than the separate doses [34]. It is worth mentioning that improved patient acceptance is one of the determining factors in the success of TB treatment and hence FDC can be considered superior although the safety and efficacy remain the same. A prospective study conducted in Indonesia, support the results of the current study and shows that although both the regimens have the same efficacy and safety profile, still the patients’ and practitioners are concerned about the number of medications taken daily in separate dose regimen that disrupt the patients’ daily activities and hence affect the treatment outcome. Furthermore, the study has also observed that gastrointestinal and muscle-joint complaints are comparatively less in the case of FDC than in separate doses [24]. Most of these studies have focused on the intensive phase of therapy for FDC regimens, yet our study shows better effects in both phases. As observed in a meta-analysis focusing on the fixed-dose combination, FDC is non-inferior to the separate dose treatment with less adverse effects and better compliance rates, hence further investigative studies are of prime importance to obtain conclusive evidence in the replacement of separate dose regimens with FDC regimen [35].

LIMITATIONS

The present study was retrospective in nature and the data was obtained from the database of one hospital. The patients included in the study were not assessed directly and hence it may lead to the unavailability of certain data related to the adverse effects. The number of patients with comorbidities was comparatively less which may not provide proper explanation of the impacts of these comorbid conditions on PTB therapy. The study was focused on the PTB and hence cannot be generalized to other forms of TB.

CONCLUSION

Our retrospective investigation has observed multiple predictors which can interact with treatment outcomes i.e., age, drug abusers, FDC and treatment interruptions. All these factors need to be addressed properly for achieving favorable results while treating PTB patients. Further studies determine the effect of FDC and other factors on drug response relationship is paramount to improve the treatment strategies.

FUNDING

This project has not received any funding.

ACKNOWLEDGMENTS

We would like to thank the Director General of Health Malaysia for his permission to publish this article.

CONFLICT OF INTEREST

No potential conflict of interest relevant to this article was reported.

Fig 1.

Figure 1.Significant predictors of unfavorable outcomes in newly diagnosed pulmonary tuberculosis (PTB) patients. This bar chart presents the hazard ratios (HRs) for key variables influencing outcomes in newly diagnosed PTB patients. Factors associated with an increased risk of unfavorable outcomes include being under 60 years of age (HR=2.21), drug abuse (HR=3.24), and treatment interruption (HR=2.702). Conversely, using fixed-dose combination (FDC) therapy during the intensive phase (HR=0.226) and throughout both treatment phases (HR=0.278) were protective factors. Albumin level (HR=0.923) demonstrated a minimal protective association. Hazard ratios above 1 indicate a higher risk of unfavorable outcomes, while those below 1 suggest protective effects.
Research in Clinical Pharmacy 2024; 2: 18-26https://doi.org/10.59931/rcp.24.0003

Table 1 Characteristics of patients with newly diagnosed PTB included into the study and significant predictors of unfavourable PTB treatment outcome in univariate and multivariate cox regression model (n=273)

Variablesn (%)PTB favourable outcomes
n=241 (88.27%)
PTB unfavourable outcomes
n=32 (11.72%)
p-valuesHR (95%CI)p-values
Male207 (75.8)181 (75.1)26 (81.3)0.301
Age, mean (SD)49.43 (15.64)48.76 (15.1)54.4 (18.5)0.054
<60 years194 (71.1)176 (73.0)18 (56.3)0.042*2.21 (1.05–4.65)0.036**
≥60 years79 (28.9)65 (27.0)14 (43.8)
Ethnicity
Malay82 (30.0)77 (32.0)5 (15.6)0.166
Chinese150 (54.9)129 (53.5)21 (65.6)
Indian41 (15.0)35 (14.5)6 (18.8)
Body weight (kg), mean (SD)50.42 (19.61)50.69 (20.36)48.29 (12.66)0.514
Smoker183 (67.0)162 (67.2)21 (65.6)0.844
Alcohol intake146 (53.5)133 (55.2)13 (40.6)0.134
Drug abuser166 (60.8)139 (57.7)27 (84.4)0.003*3.24 (1.24–8.49)0.017**
Treatment duration (months), mean (SD)5.79 (2.29)6.29 (1.53)2.03 (3.4)<0.001
PTB treatment (intensive phase)
FDC-4 tablet regimen110 (80.9)105 (84.0)5 (45.5)0.007*0.226 (0.067–0.763)0.017**
Separate tablet regimen26 (19.1)20 (16.0)2 (54.5)
PTB treatment (maintenance phase)
FDC-2 tablet128 (46.9)122 (50.6)6 (18.8)0.400
Separate tablet regimen123 (45.1)119 (49.4)4 (12.5)
PTB treatment regimen
Received FDC regimen in both phases110 (40.3)105 (43.6)5 (15.6)0.008*0.278 (0.098–0.785)0.016**
Received separate regimen29 (10.6)23 (9.5)6 (18.8)1.056 (0.400–2.785)0.913
Received FDC regimen in either phases134 (49.1)113 (46.9)21 (65.6)Reference
PTB treatment outcome
Cure161 (59.0)---
Completed80 (29.3)---
Defaulted7 (2.6)---
Treatment failure3 (1.1)---
Died22 (8.1)---
Treatment interruption39 (14.3)29 (12.0)10 (31.3)0.012*2.702 (1.186–6.154)0.018**
Concomitant diseases
Diabetes179 (65.6)161 (66.8)18 (56.3)0.242
Hypertension205 (75.1)178 (73.9)27 (84.4)0.276
HIV16 (5.9)12 (5.0)4 (12.5)0.103
Received BCG vaccine155 (93.9)145 (93.5)10 (100)1.000
Positive smear AFB at baseline198 (72.5)173 (71.8)25 (78.1)0.532
Chest X-ray at baseline
Clear5 (1.8)5 (2.1)0 (0)0.284
Mild196 (71.8)169 (70.1)27 (84.4)
Moderate57 (20.9)54 (22.4)3 (9.4)
Advance15 (5.5)13 (5.4)2 (6.3)
Positive Xpert MTB/RIF molecular test at baseline31 (72.1)25 (67.6)6 (100)0.163
Blood test results prior to PTB therapy, mean (SD)
Sodium level (mmol/L)140.67 (10.60)135.88 (6.1)134.89 (6.8)0.428
Potassium level (mmol/L)3.63 (0.25)3.89 (0.59)4.02 (0.59)0.248
Chloride level (mmol/L)102.33 (7.02)97.27 (6.01)96.94 (7.47)0.773
Urea level (mmol/L)5.47 (1.72)4.41 (3.67)7.60 (6.63)<0.0011.042 (0.991–1.095)0.107
Serum creatinine (μmol/L)85.67 (16.50)83.98 (98.67)103.81 (84.75)0.279
Fasting blood sugar (mmol/L)7.67 (6.47)7.29 (4.32)6.83 (4.15)0.566
Albumin level (g/L)26.0 (1.73)28.73 (7.15)23.54 (7.81)<0.001*0.923 (0.877–0.971)0.002**
Bilirubin level (μmol/L)13.67 (11.59)12.65 (10.29)15.56 (16.5)0.168
ALT level (U/L)50.00 (25.46)24.45 (27.8)29.41 (36.59)0.353
ALP level (U/L)237.00 (243.48)102.72 (47.25)166.06 (342.1)0.001*1.002 (1.000–1.003)0.008**
WBC level ×109/L12.17 (4.53)9.91 (5.09)8.59 (4.74)0.170
RBC level ×1012/L5.07 (0.29)4.56 (0.82)4.13 (0.68)0.0160.822 (0.437–1.547)0.541
HgB level (g/dL)13.37 (1.38)12.01 (2.19)10.88 (2.09)0.0060.921 (0.720–1.179)0.494
HCT level %41.10 (3.26)36.75 (±6.24)33.27 (±5.09)0.0120.987 (0.900–1.064)0.606
Platelet level ×109/L152.0 (84.33)373.81 (±151.18)306.03 (±154.05)0.0180.997 (0.994–1.000)0.061
Neutrophil level %80.73 (13.02)70.41 (±13.14)75.52 (±11.88)0.0381.006 (0.966–1.048)0.759
Eosinophil level ×109/L0.83 (0.50)2.36 (±4.29)1.36 (±2.01)0.332
Lymphocyte level ×109/L10.57 (8.33)18.80 (±9.65)11.93 (±8.58)0.0050.958 (0.895–1.025)0.214
Blood test result at day 14 of therapy, mean (SD)
Albumin level (g/L)29.5 (7.13)29.86 (±6.93)24.81 (±8.34)<0.0010.979 (0.894–1.072)0.624
Bilirubin level (μmol/L)8.72 (13.99)8.48 (±13.80)15.22 (±23.69)0.0201.005 (0.990–1.021)0.490
ALT level (U/L)23.13 (20.57)22.58 (±20.27)29.16 (±23.4)0.183
ALP level (U/L)99.58 (39.14)96.19 (±32.02)111.28 (±64.37)0.0321.004 (0.993–1.014)0.489

*Fisher’s exact test, **significant value in final model.

FDC=fixed-dose combination, FDC-4=fixed-dose combination consists of four drugs, SD=standard deviation, FDC-2=fixed-dose combination consists of two drugs, FDC-3=fixed-dose combination consists of three drugs, BCG=Bacillus Calmette–Guérin vaccine, ALT=alanine aminotransferase, ALP=alkaline phosphatase, WBC=white blood cells, RBC=red blood cells, HgB=haemoglobin, HCT=haematocrit. Highlighted in grey=significant variables in univariate analysis, Highlighted in blue=significant variables in the final multivariate cox regression analysis.


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