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\def\citeauthor{~Robin~George,
              ~Bhavana~Sree~G,
              ~Akshaya~S,
              ~Bhargav~C,
              ~Sunil~Kumar~E}
\def\citetitle{\textbf{Study on the Prescription Pattern and Antibiogram in the Management of Cellulitis}}
\def\copyrightstmt{This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial- ShareAlike 4.0 License, which allows others to remix, tweak, and build upon the work non-commercially, as long as the author is credited and the new creations are licensed under the identical terms.}
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\def\RunningAuthor{Robin George et al.}
\firstPage{157}
\articleType{Original Article}
\receivedDate{15 Apr 2023}
\acceptedDate{05 May 2023}
\revisedDate{04 May 2023}
\journalVolume{3}
\journalIssue{2}
\journalDoi{fjphs.v3i2.463}
\copyrightYear{2023}
\eISSN{2583-116X}
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\def\journalTitle{Future Journal of Pharmaceuticals and Health Sciences}

\title{\textbf{Study on the Prescription Pattern and Antibiogram in the Management of Cellulitis}}
\author{Robin~George\textsuperscript{*}\textsuperscript{1},  
            Bhavana~Sree~G\textsuperscript{2},  
            Akshaya~S\textsuperscript{2},  
            Bhargav~C\textsuperscript{2}, 
            Sunil~Kumar~E\textsuperscript{2}~\\[5pt]{\textsuperscript{1}Department of Pharmacy Practice, Seven Hills College of Pharmacy, Tirupati, Andhra Pradesh - 517561, India}~\\{\textsuperscript{2}Department of Pharmacy Practice, SVIMS-SPMC, Tirupati, Andhra Pradesh, India}}

\begin{abstract}
Cellulitis is a very serious bacterial infection, although seen commonly in most individuals. Recurrent episodes of cellulitis has been becoming a burden to patients as well as physicians making it difficult to treat. Managing the patients with low dose long-term antibiotic therapy based on the antimicrobial culture sensitivity reports helps to reduce the recurrent episodes of cellulitis. This study has been conducted to observe and study the antibiotic patterns prescribed to patients with cellulitis and development of resistance in recurrent episodes, thereby determining the bacterial pathogens isolated and studying it's sensitivity pattern for each recurrent episode.   This is a prospective observational study carried out in the Department of General Surgery over a period of 6 months. A total of 99 recurrent cellulitis patients were included in the study, in which 69(69.6\%) were males and 30(30.3\%) were females. Age groups between 51-60 were utmost percentage of 31.3\%(31). 38(38.4\%) Lower limb cellulitis was at great number in disease patterns among recurrent cellulitis patients. Preponderance of patients (53 patients) were prescribed with Dual antibiotic therapy (Magnex and Metrogyl). Frequency of  Staphylococcus aureus species stays first in AST reports. Antibiogram of E.Coli showed decreased sensitivity in 2\ensuremath{^{nd}} AST towards almost all antibiotics when compared with other pathogens for which the test was performed. This study concludes that rationale of antibiotic use and close monitoring is essential in the treatment of cellulitis with the help of standard treatment guidelines of SSTI's (Treatment based on AST reports) to prevent the further recurrent episodes and associated complications of cellulitis. 
\end{abstract}\def\keywordstitle{Keywords}

\begin{keywords}Prescription Pattern,\newline Antibiogram,\newline Cellulitis
\end{keywords}
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\makeatletter{\raggedright{\Large$^\ast$}Corresponding Author\par Name:\ Robin~George~\\ Phone:\ ~\\  Email:\ robin.george793@gmail.com
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\section{INTRODUCTION}
Cellulitis is one of the most common and serious bacterial infections involving deep layers of skin and tissues, thus falls into skin and soft tissue infections(SSTI) \unskip~\cite{1932490:28032314}. Cellulitis may be categorized as either purulent or non-purulent; purulent form of cellulitis is associated with abscess or pus discharge in contrast to non-purulent cellulitis \unskip~\cite{1932490:28032314}. The foot cellulitis (or) lowerlimb cellulitis has relatively more frequency when compared with other parts of the body, cellulitis was estimated to be the third most frequently managed infectious disease and it has been seen as one of the economic and health burden condition. 

The incidence rate of cellulitis has been noted to be higher in males and in individuals aged above 45 years. A large epidemiologic hospital-based study on skin, soft tissue, bone \& joint injections, concluded that 37.3\% of the patients admitted are of cellulitis. Risk factors of cellulitis includes immuno-deficiency, obesity and over-weight, lymphedema, use of illicit injectable drugs and mostly having a history of cellulitis or any other skin problems. Cellulitis is clinically presented with the acute onset of the erythema of skin, swelling, warmth \& tenderness and if it is left untreated the complications such as abscess, necrotizing fasciitis, amputation etc can be seen more commonly. Hence, treating the cellulitis as early as possible is very essential to prevent the early \& late complications. 

The 2 major pathogens causing cellulitis are streptococcus pyrogens (GAS) and staphylococcus aureus. The standard treatment for the uncomplicated cellulitis includes the short course betalactum antibiotics (for 5-7days)\unskip~\cite{1932490:28032304}. Even after the completion of the treatment, care to be taken on the cellulitic area with some non{\textendash}medicative methods like cool compressions on the infected area, bathings with lukewarm water, elevation of the infected part etc. 

Even with utmost care, recurrence of the cellulitis can occur. Two hypotheses are explained for the recurrence of the cellulitis are -1. The traditional Re-infection Hypothesis which explains that the recurrence risk is increased due to the local tissue modifications that are induced by the previous disease episodes. 2. An alternate hypothesis gives the pathophysiologic explanation which is the pathogen survival and its perseverance in the infected skin and tissues \unskip~\cite{1932490:28032308}. Hence after the first recurrence, the relapsing of the disease cannot be prevented. This directly or indirectly affects the quality of patient's everyday life. The only way to improve the patient's quality of life is sticking to the antibiotic treatment regimen, either long term or short term based on the disease severity. 

Antibiotics are the medications which are used to treat and prevent all the bacterial infections. Cephalosporins falls under most commonly prescribed class of antibiotics in cellulitis patients. When dual therapy is needed, Cefoperazone+Sulbactum and Piperacillin+Tazobactum(according to National Treatment Guidelines for Antimicrobial Use in Infectious Diseases of NCDC) were mostly prescribed treatments. However, changes are made in treatment according to the patient condition or antimicrobial culture sensitivity test(AST) reports. Despite the treatment given, some of them would fail and there may be no signs of improvement. This may imply that the bacteria in the body has developed the resistance against the antibiotic. 

Performing culture sensisitivity tests and treating the patients with specific antibiotic which is active against the organism present in the body results in effective treatment \unskip~\cite{1932490:28032306}. Culture sensitivity based antibiotic treatment helps to reduce the chance of resistance and its associated risks \unskip~\cite{1932490:28032306}. One consensus study in UK, interpreted that nearly 20\% of the patients admitted into the hospital because of the bacterial infections are mostly due to the antibiotic resistance. Hence it is most important to obtain culture test reports in any septic conditions, especially cellulitis and treat them accordingly which can even help to reduce the recurrence of infection. 

The initial episode of cellulitis can be treated with empirical regimen which is mainly active against streptococci bacteria. For cellulitis showing systemic signs, parenteral antibiotic therapy can be given and can be converted to oral antibiotics when symptoms are improved \unskip~\cite{1932490:28032308}. Nonetheless, there is a need for the culture sensitivity tests to choose the right antibiotics which in turn can also help to reduce the recurrence of infection. 

According to a meta-analysis \unskip~\cite{1932490:28032303}, antibiotic prophylaxis can reduce the risk of recurrent cellulitis when compared to no antibiotic prophylaxis. Hence, for patients with two or more episode low dose antibiotic therapy can be given by choosing the antibiotic regimen based on the patients culture reports to reduce (or) prevent the recurrence of cellulitis. Also, the significance of non-pharmacological interventions should be made known to the patients and they should be recommended to the patients for the better improvement of the clinical condition.
    
\section{METHODOLOGY}
Department of General Surgery, Sri Venkateswara Institute of Medical Sciences, SPMC(W) {\textendash}Tirupati, was selected as the field of work under the guidance of Dr B.Sriharirao and the Institutional guide Dr Robin George. The study title was selected and framed after a thorough literature review and detailed discussion concerning the practical possibilities and difficulties.

The study proposal was prepared and the approval was obtained from the Head of the Institution and the Institutional Ethical Committee. Detailed data collection forms with Informed consent were prepared. The detailed purpose of the study and benefits are explained in local language to the individual patients and care takers before obtaining informed consent without any force or compulsion. Data was collected from the patients who falls under the inclusion and exclusion criteria.

All the patients diagnosed with recurrent cellulitis attending General surgery department were enrolled for the study according to the study criteria. All the patients are examined and the demographic details, clinical features along with Antimicrobial culture reports were documented and tabulated. Antimicrobial culture sensitivity result analysis includes the Antibiotics used (from prescriptions), Course of antibiotic treatment (in days), Organism identified (based on antimicrobial culture reports), Sensitive antibiotics, Resistant antibiotics and the need of antibiotic reconciliation. All the clinical details and drug related details were analyzed for consecutive recurrent episodes statistically. These results were used to review the rationale of prescription patterns.
    
\section{RESULTS}
A total number of 99 cellulitis patient's records were assessed by considering inclusion criteria and enrolled into our study after obtaining a detailed informed consent, from the department of General Surgery.


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\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Gender categorization of subjects} }
\label{tw-06fda9dfda27}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.1936\linewidth-2\tabcolsep}p{\dimexpr.2433\linewidth-2\tabcolsep}p{\dimexpr.29320000000000004\linewidth-2\tabcolsep}p{\dimexpr.2699\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack S.NO. & \cAlignHack GENDER & \cAlignHack FREQUENCY & \cAlignHack PERCENTAGE (\%)\\
\tblmidrule 
\cAlignHack 1. &
  \cAlignHack Female &
  \cAlignHack 30 &
  \cAlignHack 30.3\\
\cAlignHack 2. &
  \cAlignHack Male &
  \cAlignHack 69 &
  \cAlignHack 69.6\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Disease patterns among subjects} }
\label{tw-8fe721ce3242}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.2201\linewidth-2\tabcolsep}p{\dimexpr.27649999999999995\linewidth-2\tabcolsep}p{\dimexpr.25340000000000003\linewidth-2\tabcolsep}p{\dimexpr.25\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack S.No. & \cAlignHack Disease & \cAlignHack FREQUENCY & \cAlignHack PERCENTAGE OF DIAGNOSIS (\%)\\
\tblmidrule 
\cAlignHack 1. &
  \cAlignHack Lower Limb Cellulitis &
  \cAlignHack 38 &
  \cAlignHack 38.4\\
\cAlignHack 2. &
  \cAlignHack Necrotizing Fascitis &
  \cAlignHack 19 &
  \cAlignHack 19.2\\
\cAlignHack 3. &
  \cAlignHack Foot Ulcer &
  \cAlignHack 19 &
  \cAlignHack 19.2\\
\cAlignHack 4. &
  \cAlignHack Cellulitis + Abscess &
  \cAlignHack 6 &
  \cAlignHack 6.1\\
\cAlignHack 5. &
  \cAlignHack Cellulitis + Ulcer &
  \cAlignHack 6 &
  \cAlignHack 6.1\\
\cAlignHack 6. &
  \cAlignHack Cellulitis + Gangrene &
  \cAlignHack 10 &
  \cAlignHack 10.1\\
\cAlignHack 7. &
  \cAlignHack Cellulitis + Verucca plantaris &
  \cAlignHack 1 &
  \cAlignHack 1.01\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Comorbidities observed among subjects} }
\label{tw-0b669455593f}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.18689999999999998\linewidth-2\tabcolsep}p{\dimexpr.2732\linewidth-2\tabcolsep}p{\dimexpr.2467\linewidth-2\tabcolsep}p{\dimexpr.2932\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack S.NO. & \cAlignHack COMORBIDITY & \cAlignHack FREQUENCY & \cAlignHack PERCENTAGE OF COMORBIDITY PRESENT\\
\tblmidrule 
\cAlignHack 1. &
  \cAlignHack Type 2 Diabetes mellitus &
  \cAlignHack 28 &
  \cAlignHack 28.3\\
\cAlignHack 2. &
  \cAlignHack Type 1 Diabetes mellitus &
  \cAlignHack 1 &
  \cAlignHack 1.01\\
\cAlignHack 3. &
  \cAlignHack Diabetes mellitus + Hypertension &
  \cAlignHack 13 &
  \cAlignHack 13.1\\
\cAlignHack 4. &
  \cAlignHack Coronary Artery Disease &
  \cAlignHack 6 &
  \cAlignHack 6.1\\
\cAlignHack 5. &
  \cAlignHack Chronic Kidney Disease &
  \cAlignHack 9 &
  \cAlignHack 9.1\\
\cAlignHack 6. &
  \cAlignHack Trauma &
  \cAlignHack 21 &
  \cAlignHack 21.2\\
\cAlignHack 7. &
  \cAlignHack Hypertension &
  \cAlignHack 16 &
  \cAlignHack 16.2\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Wound anatomy of subjects} }
\label{tw-332e2b7cf8ce}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.18020000000000003\linewidth-2\tabcolsep}p{\dimexpr.31979999999999997\linewidth-2\tabcolsep}p{\dimexpr.2533\linewidth-2\tabcolsep}p{\dimexpr.2467\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack S.NO. & \cAlignHack WOUND ANATOMY & \cAlignHack FREQUENCY & \cAlignHack PERCENTAGE OF WOUND ANATOMY\\
\tblmidrule 
\cAlignHack 1. &
  \cAlignHack Pus Discharge &
  \cAlignHack 56 &
  \cAlignHack 56.56\\
\cAlignHack 2. &
  \cAlignHack Redness &
  \cAlignHack 22 &
  \cAlignHack 22.22\\
\cAlignHack 3. &
  \cAlignHack Pus Discharge + Redness &
  \cAlignHack 8 &
  \cAlignHack 8.08\\
\cAlignHack 4. &
  \cAlignHack Blackish Discolouration &
  \cAlignHack 17 &
  \cAlignHack 17.17\\
\cAlignHack 5. &
  \cAlignHack Blackish Discolouration + Pus Discharge &
  \cAlignHack 9 &
  \cAlignHack 9.09\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Antibiotics prescribing pattern among subjects} }
\label{tw-4d44bcd1fda5}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.17689999999999998\linewidth-2\tabcolsep}p{\dimexpr.32980000000000004\linewidth-2\tabcolsep}p{\dimexpr.2433\linewidth-2\tabcolsep}p{\dimexpr.25\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack S.No & \cAlignHack Antibiotics Prescribing Pattern & \cAlignHack No. Of Patients \mbox{}\protect\newline (n=99) & \cAlignHack Percentage Of Antibiotic Prescribing Pattern\\
\tblmidrule 
\cAlignHack 1. &
  \cAlignHack Monotherapy &
  \cAlignHack 44 &
  \cAlignHack 44.4\\
\cAlignHack 2. &
  \cAlignHack Dual therapy &
  \cAlignHack 53 &
  \cAlignHack 53.5\\
\cAlignHack 3. &
  \cAlignHack Triple therapy &
  \cAlignHack 2 &
  \cAlignHack 2.02\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Antibiotic Prescribing Pattern in Monotherapy} }
\label{tw-7609d962786e}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.2069\linewidth-2\tabcolsep}p{\dimexpr.2765\linewidth-2\tabcolsep}p{\dimexpr.2666\linewidth-2\tabcolsep}p{\dimexpr.25\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack S.NO & \cAlignHack DRUGS IN MONOTHERAPY & \cAlignHack NO.OF PATIENTS (n=44) & \cAlignHack PERCENTAGE OF DRUGS (\%)\\
\tblmidrule 
\cAlignHack 1. &
  \cAlignHack Augmentin &
  \cAlignHack 11 &
  \cAlignHack 25\\
\cAlignHack 2. &
  \cAlignHack Cotrimoxazole &
  \cAlignHack 8 &
  \cAlignHack 18.2\\
\cAlignHack 3. &
  \cAlignHack Clindamycin &
  \cAlignHack 6 &
  \cAlignHack 13.6\\
\cAlignHack 4. &
  \cAlignHack Metrogyl &
  \cAlignHack 3 &
  \cAlignHack 6.8\\
\cAlignHack 5. &
  \cAlignHack Cefpodoxime proxetil &
  \cAlignHack 3 &
  \cAlignHack 6.8\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Antibiotic Prescribing Pattern in Dual therapy} }
\label{tw-3e371ba3112a}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.18360000000000003\linewidth-2\tabcolsep}p{\dimexpr.3164\linewidth-2\tabcolsep}p{\dimexpr.25\linewidth-2\tabcolsep}p{\dimexpr.25\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack S.NO. & \cAlignHack DRUGS IN DUAL THERAPY & \cAlignHack NO.OF PATIENTS (n=53) & \cAlignHack PERCENTAGE OF DRUGS(\%)\\
\tblmidrule 
\cAlignHack 1. &
  \cAlignHack Magnex and Metrogyl &
  \cAlignHack 31 &
  \cAlignHack 58.49\\
\cAlignHack 2. &
  \cAlignHack Augmentin and Metrogyl &
  \cAlignHack 7 &
  \cAlignHack 13.20\\
\cAlignHack 3. &
  \cAlignHack Amoxil and Clindamycin &
  \cAlignHack 3 &
  \cAlignHack 5.66\\
\cAlignHack 4. &
  \cAlignHack Piptaz and Metrogyl &
  \cAlignHack 3 &
  \cAlignHack 5.66\\
\cAlignHack 5. &
  \cAlignHack Farobact and Clindamycin &
  \cAlignHack 2 &
  \cAlignHack 3.77\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Antibiotic Prescribing Pattern in Triple therapy} }
\label{tw-c01f7641ec0a}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.2201\linewidth-2\tabcolsep}p{\dimexpr.2799\linewidth-2\tabcolsep}p{\dimexpr.25\linewidth-2\tabcolsep}p{\dimexpr.25\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack S.NO. & \cAlignHack DRUGS IN TRIPLE THERAPY & \cAlignHack NO. OF PATIENTS(N=2) & \cAlignHack PERCENTAGE OF DRUGS (\%)\\
\tblmidrule 
\cAlignHack 1. &
  \cAlignHack Piptaz, Clindamycin and Metrogyl &
  \cAlignHack 1 &
  \cAlignHack 50\\
\cAlignHack 2. &
  \cAlignHack Augmentin, Clindamycin and Meropenem &
  \cAlignHack 1 &
  \cAlignHack 50\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {Organisms identified based on Culture sensitivity reports} }
\label{tw-4413b8646d9d}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.13639999999999999\linewidth-2\tabcolsep}p{\dimexpr.245\linewidth-2\tabcolsep}p{\dimexpr.2186\linewidth-2\tabcolsep}p{\dimexpr.20\linewidth-2\tabcolsep}p{\dimexpr.20\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack S.No. & \cAlignHack Name of the Organism. & \cAlignHack Type of the Organism. & \cAlignHack No. of Bacteria cultured (n=131 ) & \cAlignHack Percentage of bacteria cultured (\%). \\
\tblmidrule 
\cAlignHack 1. &
  \cAlignHack Staphylococcus aureus &
  \cAlignHack Gram-Positive &
  \cAlignHack 29 &
  \cAlignHack 22.1\\
\cAlignHack 2. &
  \cAlignHack Klebsiella species &
  \cAlignHack Gram-Negative &
  \cAlignHack 17 &
  \cAlignHack 12.9\\
\cAlignHack 3. &
  \cAlignHack Escherichia coli &
  \cAlignHack Gram-Negative &
  \cAlignHack 28 &
  \cAlignHack 21.3\\
\cAlignHack 4. &
  \cAlignHack Pseudomonas species &
  \cAlignHack Gram-Negative &
  \cAlignHack 16 &
  \cAlignHack 12.2\\
\cAlignHack 5. &
  \cAlignHack Proteus mirabilis &
  \cAlignHack Gram-Negative &
  \cAlignHack 15 &
  \cAlignHack 11.4\\
\cAlignHack 6. &
  \cAlignHack Enterococcus Faecalis &
  \cAlignHack Gram-Positive &
  \cAlignHack 6 &
  \cAlignHack 4.5\\
\cAlignHack 7. &
  \cAlignHack Enterobacter species-I &
  \cAlignHack Gram-Positive &
  \cAlignHack 3 &
  \cAlignHack 2.3\\
\cAlignHack 8. &
  \cAlignHack Enterobacter species-II &
  \cAlignHack Gram-Positive &
  \cAlignHack 2 &
  \cAlignHack 1.5\\
\cAlignHack 9. &
  \cAlignHack Staphylococcus haemolyticus &
  \cAlignHack Gram-Negative &
  \cAlignHack 3 &
  \cAlignHack 2.3\\
\cAlignHack 10. &
  \cAlignHack Morganella morganii &
  \cAlignHack Gram-Negative &
  \cAlignHack 2 &
  \cAlignHack 1.5\\
\cAlignHack 11. &
  \cAlignHack Non-Fermentive gram {\textendash}ve Bacilli &
  \cAlignHack Gram-Negative &
  \cAlignHack 1 &
  \cAlignHack 0.8\\
\cAlignHack 12. &
  \cAlignHack Burkholderia &
  \cAlignHack Gram-Negative &
  \cAlignHack 2 &
  \cAlignHack 1.5\\
\cAlignHack 13. &
  \cAlignHack Beta-Haemolytic Streptococci &
  \cAlignHack Gram-Positive &
  \cAlignHack 1 &
  \cAlignHack 0.8\\
\cAlignHack 14. &
  \cAlignHack Non-Haemolytic Streptococci &
  \cAlignHack Gram-Positive &
  \cAlignHack 1 &
  \cAlignHack 0.8\\
\cAlignHack 15. &
  \cAlignHack Citrobacter &
  \cAlignHack Gram-Negative &
  \cAlignHack 1 &
  \cAlignHack 0.8\\
\cAlignHack 16. &
  \cAlignHack Enterococcus faecium &
  \cAlignHack Gram-Positive &
  \cAlignHack 1 &
  \cAlignHack 0.8\\
\cAlignHack 17. &
  \cAlignHack Pseudomonas BHI &
  \cAlignHack Gram-Negative &
  \cAlignHack 1 &
  \cAlignHack 0.8\\
\cAlignHack 18. &
  \cAlignHack Streptococcus pyogenes &
  \cAlignHack Gram-Positive &
  \cAlignHack 1 &
  \cAlignHack 0.8\\
\cAlignHack 19. &
  \cAlignHack Acinetobacter &
  \cAlignHack Gram-Negative &
  \cAlignHack 1 &
  \cAlignHack 0.8\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {1\ensuremath{^{st}} and 2\ensuremath{^{nd}} sensitivity patterns of Escherichia coli} }
\label{tw-594c9328542e}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.1622\linewidth-2\tabcolsep}p{\dimexpr.1578\linewidth-2\tabcolsep}p{\dimexpr.16\linewidth-2\tabcolsep}p{\dimexpr.16\linewidth-2\tabcolsep}p{\dimexpr.1666\linewidth-2\tabcolsep}p{\dimexpr.1934\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack S.No. & \cAlignHack Antibiotics & \multicolumn{2}{p{\dimexpr(.32\linewidth-2\tabcolsep)}}{\cAlignHack 1st time sensitivity pattern of E-Coli} & \multicolumn{2}{p{\dimexpr(.36\linewidth-2\tabcolsep)}}{\cAlignHack 2nd time sensitivity pattern of E-Coli}\\
\rowcolor{kwdboxcolor}\cAlignHack  & \cAlignHack  & \cAlignHack Frequency  & \cAlignHack Percentage  (\%) & \cAlignHack Frequency & \cAlignHack Percentage(\%)\\
\tblmidrule 
\cAlignHack 1. &
  \cAlignHack Amikacin &
  \cAlignHack 26 &
  \cAlignHack 92.8 &
  \cAlignHack 23 &
  \cAlignHack 82.1\\
\cAlignHack 2. &
  \cAlignHack Amoxy-Clav &
  \cAlignHack 19 &
  \cAlignHack 67.8 &
  \cAlignHack 16 &
  \cAlignHack 57.1\\
\cAlignHack 3. &
  \cAlignHack Ampicillin &
  \cAlignHack 17 &
  \cAlignHack 60.7 &
  \cAlignHack 16 &
  \cAlignHack 57.1\\
\cAlignHack 4. &
  \cAlignHack Cefoperazone + Sulbactum &
  \cAlignHack 20 &
  \cAlignHack 71.4 &
  \cAlignHack 14 &
  \cAlignHack 50\\
\cAlignHack 5. &
  \cAlignHack Ceftriaxone &
  \cAlignHack 12 &
  \cAlignHack 42.8 &
  \cAlignHack 10 &
  \cAlignHack 35.7\\
\cAlignHack 6. &
  \cAlignHack Ciprofloxacin &
  \cAlignHack 10 &
  \cAlignHack 35.7 &
  \cAlignHack 10 &
  \cAlignHack 35.7\\
\cAlignHack 7. &
  \cAlignHack Co-trimoxazole &
  \cAlignHack 17 &
  \cAlignHack 60.7 &
  \cAlignHack 14 &
  \cAlignHack 50\\
\cAlignHack 8. &
  \cAlignHack Gentamicin &
  \cAlignHack 27 &
  \cAlignHack 96.4 &
  \cAlignHack 27 &
  \cAlignHack 96.4\\
\cAlignHack 9. &
  \cAlignHack Meropenem &
  \cAlignHack 28 &
  \cAlignHack 100 &
  \cAlignHack 28 &
  \cAlignHack 100\\
\cAlignHack 10. &
  \cAlignHack Cefazolin &
  \cAlignHack 22 &
  \cAlignHack 78.5 &
  \cAlignHack 19 &
  \cAlignHack 67.8\\
\cAlignHack 11. &
  \cAlignHack Ertapenem &
  \cAlignHack 28 &
  \cAlignHack 100 &
  \cAlignHack 28 &
  \cAlignHack 100\\
\cAlignHack 12. &
  \cAlignHack Piperacillin + Tazobactum &
  \cAlignHack 25 &
  \cAlignHack 89.3 &
  \cAlignHack 20 &
  \cAlignHack 71.4\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {1\ensuremath{^{st}} and 2\ensuremath{^{nd}} sensitivity patterns of Staphylococcus aureus} }
\label{tw-4844ff9f1a6c}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.1291\linewidth-2\tabcolsep}p{\dimexpr.1798\linewidth-2\tabcolsep}p{\dimexpr.15999999999999998\linewidth-2\tabcolsep}p{\dimexpr.1711\linewidth-2\tabcolsep}p{\dimexpr.1821\linewidth-2\tabcolsep}p{\dimexpr.1779\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack S.No. & \cAlignHack ANTIBIOTICS & \multicolumn{2}{p{\dimexpr(.3311\linewidth-2\tabcolsep)}}{\cAlignHack 1ST TIME SENSITIVITY PATTERN OF S.AUREUS} & \multicolumn{2}{p{\dimexpr(.36\linewidth-2\tabcolsep)}}{\cAlignHack 2ND TIME SENSITIVITY PATTERN OF S.AUREUS}\\
\rowcolor{kwdboxcolor}\cAlignHack  & \cAlignHack  & \cAlignHack Frequency  & \cAlignHack Percentage  (\%) & \cAlignHack Frequency & \cAlignHack Percentage(\%)\\
\tblmidrule 
\cAlignHack 1. &
  \cAlignHack Ampicillin &
  \cAlignHack 5 &
  \cAlignHack 17.2 &
  \cAlignHack 3 &
  \cAlignHack 10.3\\
\cAlignHack 2. &
  \cAlignHack Cefoxitin &
  \cAlignHack 25 &
  \cAlignHack 86.2 &
  \cAlignHack 27 &
  \cAlignHack 93.1\\
\cAlignHack 3. &
  \cAlignHack Ciprofloxacin &
  \cAlignHack 11 &
  \cAlignHack 37.9 &
  \cAlignHack 10 &
  \cAlignHack 34.4\\
\cAlignHack 4. &
  \cAlignHack Clindamycin &
  \cAlignHack 18 &
  \cAlignHack 62.0 &
  \cAlignHack 15 &
  \cAlignHack 51.7\\
\cAlignHack 5. &
  \cAlignHack Co-trimoxazole &
  \cAlignHack 23 &
  \cAlignHack 79.3 &
  \cAlignHack 20 &
  \cAlignHack 68.9\\
\cAlignHack 6. &
  \cAlignHack Erythromycin &
  \cAlignHack 22 &
  \cAlignHack 75.8 &
  \cAlignHack 20 &
  \cAlignHack 68.9\\
\cAlignHack 7. &
  \cAlignHack Gentamicin &
  \cAlignHack 25 &
  \cAlignHack 86.2 &
  \cAlignHack 24 &
  \cAlignHack 82.7\\
\cAlignHack 8. &
  \cAlignHack Linezolid &
  \cAlignHack 28 &
  \cAlignHack 96.5 &
  \cAlignHack 29 &
  \cAlignHack 100\\
\cAlignHack 9. &
  \cAlignHack Penicillin &
  \cAlignHack 12 &
  \cAlignHack 41.3 &
  \cAlignHack 9 &
  \cAlignHack 31.0\\
\cAlignHack 10. &
  \cAlignHack Tetracycline &
  \cAlignHack 27 &
  \cAlignHack 93.1 &
  \cAlignHack 27 &
  \cAlignHack 93.1\\
\cAlignHack 11. &
  \cAlignHack Vancomycin &
  \cAlignHack 27 &
  \cAlignHack 93.1 &
  \cAlignHack 27 &
  \cAlignHack 93.1\\
\cAlignHack 12. &
  \cAlignHack Tigecycline &
  \cAlignHack 22 &
  \cAlignHack 75.8 &
  \cAlignHack 21 &
  \cAlignHack 72.4\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {1\ensuremath{^{st}} and 2\ensuremath{^{nd}} sensitivity patterns of Klebsiella species} }
\label{tw-1ccef8fb273d}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.16\linewidth-2\tabcolsep}p{\dimexpr.16\linewidth-2\tabcolsep}p{\dimexpr.16\linewidth-2\tabcolsep}p{\dimexpr.16\linewidth-2\tabcolsep}p{\dimexpr.1578\linewidth-2\tabcolsep}p{\dimexpr.2022\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack S.No. & \cAlignHack Antibiotics & \multicolumn{2}{p{\dimexpr(.32\linewidth-2\tabcolsep)}}{\cAlignHack 1st time sensitivity pattern of Klebsiella species} & \multicolumn{2}{p{\dimexpr(.36\linewidth-2\tabcolsep)}}{\cAlignHack 2nd time sensitivity pattern of Klebsiella species}\\
\rowcolor{kwdboxcolor}\cAlignHack  & \cAlignHack  & \cAlignHack Frequency  & \cAlignHack Percentage  (\%) & \cAlignHack Frequency & \cAlignHack Percentage (\%)\\
\tblmidrule 
\cAlignHack 1. &
  \cAlignHack Amikacin &
  \cAlignHack 12 &
  \cAlignHack 70.5 &
  \cAlignHack 11 &
  \cAlignHack 64.7\\
\cAlignHack 2. &
  \cAlignHack Amoxy-Clav &
  \cAlignHack 10 &
  \cAlignHack 58.8 &
  \cAlignHack 10 &
  \cAlignHack 58.8\\
\cAlignHack 3. &
  \cAlignHack Ampicillin &
  \cAlignHack 13 &
  \cAlignHack 76.4 &
  \cAlignHack 12 &
  \cAlignHack 70.5\\
\cAlignHack 4. &
  \cAlignHack Cefoperazone + Sulbactum &
  \cAlignHack 12 &
  \cAlignHack 70.5 &
  \cAlignHack 8 &
  \cAlignHack 47.0\\
\cAlignHack 5. &
  \cAlignHack Ceftriaxone &
  \cAlignHack 12 &
  \cAlignHack 70.5 &
  \cAlignHack 10 &
  \cAlignHack 58.8\\
\cAlignHack 6. &
  \cAlignHack Ciprofloxacin &
  \cAlignHack 10 &
  \cAlignHack 58.8 &
  \cAlignHack 9 &
  \cAlignHack 52.4\\
\cAlignHack 7. &
  \cAlignHack Co-trimoxazole &
  \cAlignHack 9 &
  \cAlignHack 52.4 &
  \cAlignHack 9 &
  \cAlignHack 52.4\\
\cAlignHack 8. &
  \cAlignHack Gentamicin &
  \cAlignHack 11 &
  \cAlignHack 64.7 &
  \cAlignHack 12 &
  \cAlignHack 70.5\\
\cAlignHack 9. &
  \cAlignHack Meropenem &
  \cAlignHack 14 &
  \cAlignHack 82.3 &
  \cAlignHack 11 &
  \cAlignHack 64.7\\
\cAlignHack 10. &
  \cAlignHack Piperacillin + Tazobactum &
  \cAlignHack 14 &
  \cAlignHack 82.3 &
  \cAlignHack 9 &
  \cAlignHack 52.9\\
\cAlignHack 11. &
  \cAlignHack Cefazolin &
  \cAlignHack 13 &
  \cAlignHack 76.4 &
  \cAlignHack 10 &
  \cAlignHack 58.8\\
\cAlignHack 12. &
  \cAlignHack Levofloxacin &
  \cAlignHack 17 &
  \cAlignHack 100 &
  \cAlignHack 17 &
  \cAlignHack 100\\
\cAlignHack 13. &
  \cAlignHack Ertapenem &
  \cAlignHack 17 &
  \cAlignHack 100 &
  \cAlignHack 17 &
  \cAlignHack 100\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {1\ensuremath{^{st}} and 2\ensuremath{^{nd}} sensitivity patterns of Pseudomonas species} }
\label{tw-9f1f9589334a}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{p{\dimexpr.16\linewidth-2\tabcolsep}p{\dimexpr.16\linewidth-2\tabcolsep}p{\dimexpr.16\linewidth-2\tabcolsep}p{\dimexpr.16\linewidth-2\tabcolsep}p{\dimexpr.1688\linewidth-2\tabcolsep}p{\dimexpr.1912\linewidth-2\tabcolsep}}
\tbltoprule \rowcolor{kwdboxcolor}S.No. & Antibiotics & \multicolumn{2}{p{\dimexpr(.32\linewidth-2\tabcolsep)}}{1st time sensitivity pattern of Pseudomonas species} & \multicolumn{2}{p{\dimexpr(.36\linewidth-2\tabcolsep)}}{2nd time sensitivity pattern of Pseudomonas species.}\\
\rowcolor{kwdboxcolor} &  & Frequency  & Percentage  (\%) & Frequency & Percentage(\%)\\
\tblmidrule 
1. &
  Amikacin &
  14 &
  87.5 &
  13 &
  81.2\\
2. &
  Aztreonam &
  15 &
  93.7 &
  15 &
  93.7\\
3. &
  Carbenicillin &
  16 &
  100 &
  16 &
  100\\
4. &
  Cefoperazone + Sulbactum &
  12 &
  75 &
  9 &
  56.2\\
5. &
  Cefotaxime &
  7 &
  43.7 &
  6 &
  37.5\\
6. &
  Ceftazidime &
  10 &
  62.5 &
  10 &
  62.5\\
7. &
  Ciprofloxacin &
  6 &
  37.5 &
  5 &
  31.2\\
8. &
  Erithromycin &
  15 &
  93.7 &
  12 &
  75\\
9. &
  Gentamicin &
  14 &
  87.5 &
  11 &
  64.7\\
10. &
  Imipenem &
  16 &
  100 &
  16 &
  100\\
11. &
  Meropenem &
  14 &
  87.5 &
  12 &
  75\\
12. &
  Netilmycin &
  14 &
  87.5 &
  14 &
  87.5\\
13. &
  Piperacillin + Tazobactum &
  9 &
  56.2 &
  7 &
  43.7\\
14. &
  Polymyxin-B &
  16 &
  100 &
  15 &
  93.7\\
15. &
  Tobramycin &
  16 &
  100 &
  16 &
  100\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\begin{table*}[!htbp]
\caption{\boldmath {1\ensuremath{^{st}} and 2\ensuremath{^{nd}} sensitivity patterns of Proteus mirabilis} }
\label{tw-dbdc73ed8334}
\def\arraystretch{1.1}
\ignorespaces 
\centering 
\begin{tabulary}{\linewidth}{LLLLLL}
\tbltoprule \rowcolor{kwdboxcolor}\cAlignHack S.No. & \cAlignHack ANTIBIOTICS & \multicolumn{2}{p{\dimexpr(\mcWidth{3}+\mcWidth{4})}}{\cAlignHack 1ST TIME SENSITIVITY PATTERN OF PROTEUS MIRABILIS.} & \multicolumn{2}{p{\dimexpr(\mcWidth{5}+\mcWidth{6})}}{\cAlignHack 2ND TIME SENSITIVITY PATTERN OF PROTEUS MIRABILIS.}\\
\rowcolor{kwdboxcolor}\cAlignHack  & \cAlignHack  & \cAlignHack Frequency  & \cAlignHack Percentage (\%) & \cAlignHack Frequency & \cAlignHack Percentage (\%)\\
\tblmidrule 
\cAlignHack 1. &
  \cAlignHack Amikacin &
  \cAlignHack 14 &
  \cAlignHack 93.3 &
  \cAlignHack 13 &
  \cAlignHack 86.6\\
\cAlignHack 2. &
  \cAlignHack Amoxy-Clav &
  \cAlignHack 11 &
  \cAlignHack 73.3 &
  \cAlignHack 11 &
  \cAlignHack 73.3\\
\cAlignHack 3. &
  \cAlignHack Ampicillin &
  \cAlignHack 9 &
  \cAlignHack 60 &
  \cAlignHack 7 &
  \cAlignHack 46.6\\
\cAlignHack 4. &
  \cAlignHack Cefoperazone+Sulbactum &
  \cAlignHack 12 &
  \cAlignHack 80 &
  \cAlignHack 7 &
  \cAlignHack 46.6\\
\cAlignHack 5. &
  \cAlignHack Ceftriaxone &
  \cAlignHack 11 &
  \cAlignHack 73.3 &
  \cAlignHack 10 &
  \cAlignHack 66.6\\
\cAlignHack 6. &
  \cAlignHack Ciprofloxacin &
  \cAlignHack 11 &
  \cAlignHack 73.3 &
  \cAlignHack 11 &
  \cAlignHack 73.3\\
\cAlignHack 7. &
  \cAlignHack Co-trimoxazole &
  \cAlignHack 10 &
  \cAlignHack 66.6 &
  \cAlignHack 10 &
  \cAlignHack 66.6\\
\cAlignHack 8. &
  \cAlignHack Gentamicin &
  \cAlignHack 11 &
  \cAlignHack 73.3 &
  \cAlignHack 9 &
  \cAlignHack 60\\
\cAlignHack 9. &
  \cAlignHack Meropenem &
  \cAlignHack 14 &
  \cAlignHack 93.3 &
  \cAlignHack 14 &
  \cAlignHack 93.3\\
\cAlignHack 10. &
  \cAlignHack Piperacillin+Tazobactum &
  \cAlignHack 13 &
  \cAlignHack 86.6 &
  \cAlignHack 10 &
  \cAlignHack 66.6\\
\cAlignHack 11. &
  \cAlignHack Cefazolin &
  \cAlignHack 15 &
  \cAlignHack 100 &
  \cAlignHack 14 &
  \cAlignHack 93.3\\
\cAlignHack 12. &
  \cAlignHack Levofloxacin &
  \cAlignHack 15 &
  \cAlignHack 100 &
  \cAlignHack 14 &
  \cAlignHack 93.3\\
\cAlignHack 13. &
  \cAlignHack Ertapenem &
  \cAlignHack 15 &
  \cAlignHack 100 &
  \cAlignHack 15 &
  \cAlignHack 100\\
\tblbottomrule 
\end{tabulary}\par 
\end{table*}

\bgroup
\fixFloatSize{images/02425dd8-b5a0-4366-b1cc-192d459bf7b2-upicture1.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/02425dd8-b5a0-4366-b1cc-192d459bf7b2-upicture1.png}{\includegraphics{images/02425dd8-b5a0-4366-b1cc-192d459bf7b2-upicture1.png}}{}
\makeatother 
\caption{\boldmath {Frequency of distribution of age among the subjects}}
\label{f-48bd800d95bf}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/9fa3f3a1-384d-4d45-b56e-67d13430a70c-upicture2.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/9fa3f3a1-384d-4d45-b56e-67d13430a70c-upicture2.png}{\includegraphics{images/9fa3f3a1-384d-4d45-b56e-67d13430a70c-upicture2.png}}{}
\makeatother 
\caption{\boldmath {Distribution of subjects based on gender}}
\label{f-03bb3da996dc}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/6130d455-88a2-439d-b6dd-5637d470c6b7-upicture3.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/6130d455-88a2-439d-b6dd-5637d470c6b7-upicture3.png}{\includegraphics{images/6130d455-88a2-439d-b6dd-5637d470c6b7-upicture3.png}}{}
\makeatother 
\caption{\boldmath {Distribution of disease patterns}}
\label{f-9a3edc8cfb2f}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/df85e580-bc7e-421b-8a1b-124213a78642-upicture4.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/df85e580-bc7e-421b-8a1b-124213a78642-upicture4.png}{\includegraphics{images/df85e580-bc7e-421b-8a1b-124213a78642-upicture4.png}}{}
\makeatother 
\caption{\boldmath {Distribution of comorbidities among subjects}}
\label{f-edaddecaa28d}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/d254d020-9d28-4249-a842-8b88a7f21d24-upicture5.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/d254d020-9d28-4249-a842-8b88a7f21d24-upicture5.png}{\includegraphics{images/d254d020-9d28-4249-a842-8b88a7f21d24-upicture5.png}}{}
\makeatother 
\caption{\boldmath {Categorization of wound anatomy among subjects}}
\label{f-b404017d9770}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/2ab5192c-86ed-48a0-9b68-f8c2bf833d0f-upicture6.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/2ab5192c-86ed-48a0-9b68-f8c2bf833d0f-upicture6.png}{\includegraphics{images/2ab5192c-86ed-48a0-9b68-f8c2bf833d0f-upicture6.png}}{}
\makeatother 
\caption{\boldmath {Pattern of use of Antibiotics among subjects}}
\label{f-fc8063c64ca2}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/6792ee4b-30cb-4223-9a2a-4a48f5d88978-upicture7.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/6792ee4b-30cb-4223-9a2a-4a48f5d88978-upicture7.png}{\includegraphics{images/6792ee4b-30cb-4223-9a2a-4a48f5d88978-upicture7.png}}{}
\makeatother 
\caption{\boldmath {Distribution of antibiotic prescribing pattern in Monotherapy}}
\label{f-75ffecb0c1f9}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/a7538c78-af80-435a-b4ae-33fe5a5878b6-upicture8.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/a7538c78-af80-435a-b4ae-33fe5a5878b6-upicture8.png}{\includegraphics{images/a7538c78-af80-435a-b4ae-33fe5a5878b6-upicture8.png}}{}
\makeatother 
\caption{\boldmath {Percentage of Drugs in dual therapy (\%)}}
\label{f-228643d35b58}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/ce83e3d2-1ca4-421e-b5cb-33a28a5ceb6b-upicture9.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/ce83e3d2-1ca4-421e-b5cb-33a28a5ceb6b-upicture9.png}{\includegraphics{images/ce83e3d2-1ca4-421e-b5cb-33a28a5ceb6b-upicture9.png}}{}
\makeatother 
\caption{\boldmath {Distribution of antibiotic prescribing pattern in Triple therapy}}
\label{f-5e76ff8af836}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/c533a0fc-a084-4ba5-84f4-e4eb58ce2cd6-upicture10.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/c533a0fc-a084-4ba5-84f4-e4eb58ce2cd6-upicture10.png}{\includegraphics{images/c533a0fc-a084-4ba5-84f4-e4eb58ce2cd6-upicture10.png}}{}
\makeatother 
\caption{\boldmath {Distribution of Organisms identified based on Culture sensitivity reports}}
\label{f-3846f1696a71}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/3bd8fef4-fbdc-48ea-8f6f-01c5f6ae278c-upicture11.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/3bd8fef4-fbdc-48ea-8f6f-01c5f6ae278c-upicture11.png}{\includegraphics{images/3bd8fef4-fbdc-48ea-8f6f-01c5f6ae278c-upicture11.png}}{}
\makeatother 
\caption{\boldmath {Antibiogram of Escherichia coli}}
\label{f-2bd546fa0f2c}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/9efec238-5048-48a1-89d3-34ce4a055b37-upicture12.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/9efec238-5048-48a1-89d3-34ce4a055b37-upicture12.png}{\includegraphics{images/9efec238-5048-48a1-89d3-34ce4a055b37-upicture12.png}}{}
\makeatother 
\caption{\boldmath {Antibiogram of Staphylococcus aureus}}
\label{f-9189dc7ff8d0}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/bfc2129e-a9b8-4c85-a86f-82f8d6eb1c09-upicture13.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/bfc2129e-a9b8-4c85-a86f-82f8d6eb1c09-upicture13.png}{\includegraphics{images/bfc2129e-a9b8-4c85-a86f-82f8d6eb1c09-upicture13.png}}{}
\makeatother 
\caption{\boldmath {Antibiogram of Klebsiella species}}
\label{f-4feb88899bf4}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/ad73df29-98f6-4726-9af4-7a44ab812d31-upicture14.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/ad73df29-98f6-4726-9af4-7a44ab812d31-upicture14.png}{\includegraphics{images/ad73df29-98f6-4726-9af4-7a44ab812d31-upicture14.png}}{}
\makeatother 
\caption{\boldmath {Antibiogram of Pseudomonas species}}
\label{f-1ace1b0a68f9}
\end{figure}
\egroup

\bgroup
\fixFloatSize{images/b5e36583-46a4-431f-9370-95d794cd129e-upicture15.png}
\begin{figure}[!htbp]
\centering \makeatletter\IfFileExists{images/b5e36583-46a4-431f-9370-95d794cd129e-upicture15.png}{\includegraphics{images/b5e36583-46a4-431f-9370-95d794cd129e-upicture15.png}}{}
\makeatother 
\caption{\boldmath {Antibiogram of Proteus mirabilis}}
\label{f-a6e3f340ed00}
\end{figure}
\egroup
\textbf{Frequency of Age Distribution}

A total of 99 subjects were enrolled in the study, among which the highest number of subjects i.e.,  31(31.3\%) and 30(30.3\%) were in the age group of 51-60 and 61-70 respectively, followed by the age groups of 41-50(15.1\%) and 71-80(14.1\%). The least number of subjects were found in the age group of 21-30(2.02\%) as shown in the Table~\ref{tw-ddbe593e73f4}  and Figure~\ref{f-48bd800d95bf}.

\textbf{Frequency of Gender Distribution}

Out of 99 subjects with Cellulitis, 30(30.3\%) were females and 69 (69.6\%) were males as shown in Table~\ref{tw-06fda9dfda27}  and Figure~\ref{f-03bb3da996dc}.

\textbf{Disease Patterns of Cellulitis}

The different disease patterns were observed among the Recurrent Cellulitis patients. Out of 99 patients, we found 38.4\% with Lower Limb Cellulitis(38), 19.19\% with Necrotizing Fascitis and Foot Ulcer(19), 6.1\% with Cellulitis + Abscess(6), 6.1\% with Cellulitis + Ulcer(6), 10.1\% with Cellulitis + Gangrene(10), 1.01\% with Cellulitis + Verucca plantaris as shown in Table~\ref{tw-8fe721ce3242} and Figure~\ref{f-9a3edc8cfb2f}. 

\textbf{Comorbidities}

Out of 99 patients, it was observed that the highest percentage  28.3\% (28) of patients having comorbidity of Type II Diabetes mellitus followed by 21.2\% (21) of patients having Trauma, 16.2\%(16) of patients having hypertension, 13.1\% (13) of patients having Diabetes mellitus + Hypertension, 9.09\% (9) of patients having Chronic Kidney Disease, 6.1\%(6) of patients having Coronary Artery Disease. While patients with Type I~Diabetes mellitus 1.01\%(1) were found least (Table~\ref{tw-0b669455593f}  and Figure~\ref{f-edaddecaa28d}).

\textbf{Wound Anatomy}

A total number of 99 patients with Recurrent cellulitis were included in the study and their wound anatomy was observed. We found that 56.56\% of patients having Pus Discharge(56), 22.22\% of patients having Redness(22), 17.17\% of patients having Blackish Discolouration(17), 9.09\% of patients having Blackish Discolouration + Pus Discharge(9) and 8.08\% of patients having Pus Discharge + Redness(8) (Table~\ref{tw-332e2b7cf8ce}  and Figure~\ref{f-b404017d9770}).

\textbf{Antibiotic Prescribing Pattern Based on Regimen}

Among all the 99 patients who were on Antibiotic treatment, we observed utmost percentage of 53.53\% of patients receiving antibiotics of dual regimen followed by 44.44\% of monotherapy and 2.02\% of Triple therapy (Table~\ref{tw-4d44bcd1fda5}  and Figure~\ref{f-fc8063c64ca2}).

\textbf{Antibiotics Prescribing Pattern in Monotherapy}

Among the Monotherapy prescriptions, the most commonly prescribed antibiotics were 11(25\%) Augmentin followed by Cotrimoxazole 8(18.2\%) and Clindamycin 6(13.6\%) followed by Metrogyl 3(6.8\%)  and Cefpodoxime-proxetil 3(6.8\%) (Table~\ref{tw-7609d962786e}  and Figure~\ref{f-75ffecb0c1f9}).

\textbf{Antibiotics Prescribing Pattern in Dual Therapy}

Among the dual therapy prescriptions, most commonly prescribed drugs were 31(58.49\%) Magnex and Metrogyl followed by 7(13.20\%) Augmentin and Metrogyl and 3 (5.66\%) for both Amoxil and Clindamycin, Piptaz and Metrogyl and the least frequently prescribed dual therapy was 2 (3.77\%) Farobact and Clindamycin as shown in Table~\ref{tw-3e371ba3112a} and Figure~\ref{f-228643d35b58}.

\textbf{Antibiotics Prescribing Pattern in Triple Therapy}

Out of 99 prescriptions, 2 prescriptions were having triple therapy. Most commonly prescribed drugs were 1 (50\%) Piptaz, Clindamycin and Metrogyl and the another drugs were Augmentin, Clindamycin and Meropenem (Table~\ref{tw-c01f7641ec0a}  and Figure~\ref{f-5e76ff8af836}).

\textbf{Culture Sensitivity Patterns}

Out of 99 subjects in the study, AST is performed for all the subjects, in which 84 subjects showed microbial growth involving various pathogens and no organisms were found in 15 subjects. Out of these 84 culture reports, the most frequently isolated organism was S.aureus 29 times(22.1\%), followed by E-coli 28 times(21.3\%), Klebsiella species 17times(12.9\%), Pseudomonas species 16 times(12.2\%) and Proteus mirabilis 15 times(11.4\%). The least frequently isolated organisms were B-haemolytic species(0.8\%), Enterococcus faecium(0.8\%), Streptococcus pyogenes(0.8\%) and so on as shown in Table~\ref{tw-4413b8646d9d} and Figure~\ref{f-3846f1696a71}.

\textbf{Antibiogram of Escherichia coli (n=28)}

E-coli was identified in 28 cultures and the sensitivity test was performed 2 times. Sensitivity rate was decreased in almost all antibiotics showing that the organism has developed resistance to remaining antibiotics. Sensitivity rate was mostly decreased for the drugs cefaperazone+sulbactum, followed by Piperacillin+Tazobactum as shown in Table~\ref{tw-594c9328542e} and Figure~\ref{f-2bd546fa0f2c}.

\textbf{Antibiogram of Staphylococcus aureus (n=29)}

Out of 29 cultures in which S.aureus was identified, the sensitivity rate was decreased in almost all antibiotics. The sensitivity rate was increased in 2 antibiotics showing that the treatment has been effective after 1\ensuremath{^{st}} culture sensitivity test (Table~\ref{tw-4844ff9f1a6c}  and Figure~\ref{f-9189dc7ff8d0}).

\textbf{Antibiogram of Klebsiella species (n=17)}

Out of 17 cultures in which klebsiella species was identified, Cefoperazone+sulbactum and Piperacillin+Tazobactum showed more decrease in sensitivity rate in 2\ensuremath{^{nd}} culture when compared to 1\ensuremath{^{st}} one. Other antibiotics showed minimal decrease in sensitivity pattern as shown in Table~\ref{tw-1ccef8fb273d} and Figure~\ref{f-4feb88899bf4}.

\textbf{Antibiogram of Pseudomonas species (n=16)}

Out of 16 cultures in which pseudomonas species was identified, the sensitivity rate was decreased in only some of the antibiotics showing that the treatment regimen was effective towards pseudomonas species which was prescribed after 1\ensuremath{^{st}} culture sensitivity test as shown in Table~\ref{tw-9f1f9589334a} and Figure~\ref{f-1ace1b0a68f9}.

\textbf{Antibiogram of Proteus mirabilis (n=15) }

Out of 15 cultures in which Proteus mirabilis was identified, almost all antibiotics showed decreased sensitivity rate in the 2\ensuremath{^{nd}} culture sensitivity test when compared to 1\ensuremath{^{st}} culture test (Table~\ref{tw-dbdc73ed8334}  and Figure~\ref{f-a6e3f340ed00}).
    
\section{DISCUSSION}
The present study was carried out with 99 patients who were presented with recurrent episodes of cellulitis of leg to General surgery department of SVIMS-SPMC(W), Tirupati. We carried out this study as Antimicrobial resistance and irrelevant use of antibiotics can be the causes for recurrence of cellulitis.

Cellulitis is one of the most common and serious bacterial infection involving deep layers of skin and tissues, thus falls into Skin and Soft Tissue Infections. Recurrent Cellulitis is the cellulitis which is reappearing again after the 1\ensuremath{^{st}} episode. Many pre-disposing factors are responsible for the relapse of the cellulitis, in which the fail of the antibiotic treatment stands at first in most of the cases followed by the associated comorbidities.

In this study we found that the recurrent episodes were most frequently found in the male subjects of age above 50 years. Many literature and surveys concluded that patients of age above 50 years are more prone to infections such as cellulitis. A study conducted by Ellis Simonsen, et. al(2005) on cellulitis incidence in a defined population, concluded that the cellulitis incidence rate is higher in individuals aged 45-64 years.

Considering the disease patterns, in our study we observed that 38(38.38\%) patients with lower limb cellulitis, 19(19.19\%) patients with Necrotizing Fascitis and Foot ulcer, 10(10.10\%) patients with Cellulitis + Gangrene, 6(6.06\%) patients with Cellulitis + Abscess and Cellulitis + Ulcer and 1(1.01\%) patient with Cellulitis + Verucca plantaris.

Moreover, in this study 44(44.44\%) prescriptions had Antibiotic monotherapy, 53(53.53\%) prescriptions had Antibiotic dual therapy and only 2(2.02) prescriptions had Triple therapy.

Furthermore, commonly prescribed drug combination patterns were analysed, out of 99 prescriptions, 44 are monotherapy antibiotics, among these 11(25\%) were Augmentin, 8(18.18\%) were cotrimoxazole, 6(13.63\%) were Clindamycin, 3(6.81\%)were Metrogyl and 3(6.81\%) were Cefpodoximeproxetil. Among 53 dual therapy antibiotics, 31(58.49\%) were Magnex and Metrogyl, 7(13.20\%) were Augmentin and Metrogyl, 3(5.66\%) were Amoxil and Clindamycin, Piptaz and Metrogyl and 2(3.77\%) were Farobact and Clindamycin. Among 2 triple therapy antibiotics, 1(50\%) is Piptaz, Clindamycin and Metrogyl and 1(50\%) is Augmentin, Clindamycin and Meropenem.

Considering the culture sensitivity patterns, out of 99 patients, Organisms were identified in 84 patients and no organisms were found in 15 patients. Their reports were analyzed for Cultured bacterial pathogens, 29(22.1\%) cultures had Staphylococcus aureus, 28(21.3\%) cultures had Escherichia coli, 17(12.9\%) cultures had Klebsiella species, 16(12.2\%) cultures had Pseudomonas species and 15(11.4\%) cultures had Proteus mirabilis.

A study conducted by D.Mishra et.al., on Antibiotic resistance pattern of bacterial isolates from Skin and Soft Tissue Infections, concluded that Staphylococcus aureus was the commonest pathogen. Our study population also reflects the same that 29(22.1\%) cultures had Staphylococcus aureus.

Considering comorbidities, it was observed that the highest percentage 28.3\% (28) of patients having  Type II Diabetes mellitus followed by 21.2\% (21) of patients having trauma, 16.2\% (16) of patients having hypertension, 13.1\% (13) of patients having  Diabetes mellitus + Hypertension, 9.1\%(9) of patients having Chronic Kidney Disease, 6.1\%(6) of patients having Coronary Artery Disease. While patients with Type I~Diabetes mellitus 1.01\% (1) was found atleast. A study conducted by G.Zacay et.al.,(2020) on Interpretation of Correlation between the level of Glycemic control and Rate of Infection, concluded that HbA1C greater than 7.5\% was associated with a 1.4 fold increased risk of cellulitis and 1.12 fold increased risk of cellulitis for every 1\% elevation in HbA1C. In our study also the highest percentage 28.28\% (28) of patients having comorbidity of Type II Diabetes mellitus.

By filtering out the Antibiogram of most common micro-organisms, E.Coli showed decreased sensitivity percentage in 2\ensuremath{^{nd}} AST towards almost all antibiotics compared to 1\ensuremath{^{st}} AST  for which the test was performed. Out of 12 antibiotics, Staphylococcus aureus showed decreased sensitivity percentage in 2\ensuremath{^{nd}} AST for 8 antibiotics. 

Out of 13 antibiotics, Klebsiella species showed decreased sensitivity percentage in 2\ensuremath{^{nd}} AST for 8 antibiotics. Out of 13 antibiotics, Proteus mirabilis showed decreased sensitivity percentage in 2\ensuremath{^{nd}} AST for 8 antibiotics. Out of 15 antibiotics, Pseudomonas species showed decreased sensitivity percentage in 2\ensuremath{^{nd}} AST for 9 antibiotics. 

In all the above organisms which were more frequently isolated, cefoperazone+sulbactum and Piperacillin+Tazobactum showed higher resistance rate in 2\ensuremath{^{nd}} AST compared to 1\ensuremath{^{st}} AST. 

A Comparative study that is conducted by Huili Zhang et.al., on the changes of bacterial species and severity of antimicrobial resistance during 13 years has interpreted that the resistance rate of some bacteria to piperacillin/tazobactam, cefoperazone/sulbactam has increased significantly followed by other antibiotics.
    
\section{CONCLUSION}
Our prospective observational study concluded that the antibiotics were most effective in the treatment of cellulitis in adjuvant with surgical interventions and other non-pharmacological interventions. Dual therapy was the most prescribed antibiotic regimen followed by monotherapy and triple therapy. It was found that augmentin was most frequently prescribed as monotherapy and Magnex forte + metronidazole was most frequently prescribed as dual therapy. Antimicrobial culture sensitivity test was performed and out of 99 subjects, no organism was identified in 15 subjects while 84 of them showed various organisms that were isolated with the help of pus and tissue cultures. The most frequently isolated bacterial pathogens were staphylococcus aureus and Escherichia coli followed by Klebsiella species, pseudomonas species and proteus mirabilis. The antibiogram of Escherichia coli showed that the sensitivity rate is decreased when AST is performed for 2\ensuremath{^{nd}} time compared with that of 1\ensuremath{^{st}} one, showing that the bacteria has developed resistance towards remaining antibiotics. Cefoperazone+sulbactum and Piperacillin + tazobactum showed high resistance rate compared to others.  The antibiogram of staphylococcus also showed the utmost decline in sensitivity rate of 2\ensuremath{^{nd}} AST towards Cefoperazone+sulbactum and Piperacillin+tazobactum similar to E-coli. Klebsiella species, Pseudomonas species and proteus mirabilis also showed similar sensitivity patterns. This study concludes that rationale of antibiotic use and close monitoring is essential in the treatment of cellulitis with the help of standard treatment guidelines of SSTI's (Treatment based on AST reports) to prevent the further recurrent episodes and associated complications of cellulitis.

\textbf{Conflict of interest}

No authors declared conflict of interest.

\textbf{Funding}

None.
    

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