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 Table of Contents    
ORIGINAL ARTICLE  
Year : 2018  |  Volume : 11  |  Issue : 4  |  Page : 253-264
International classification of diseases-based audit of the injury database to understand the injury distribution in patients who have sustained a head injury (International Classification of Diseases Codes: S00-S09)


1 Department of Community Medicine, ESIC Medical College and Hospital, Faridabad, Haryana, India
2 Department of Community Medicine, MGM Medical College and Hospital, Kishanganj, Bihar, India
3 Department of Community Medicine, Katuri Medical College, Guntur, Andhra Pradesh, India
4 Department of Statistics, Bhabha Atomic Research Centre, Mumbai, Maharashtra, India
5 Bangalore Unit, ICMR-National Institute of Virology, Bengaluru, India
6 Department of Neurosurgery, Narayana Medical College Hospital, Nellore, Andhra Pradesh, India

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Date of Submission17-Aug-2018
Date of Acceptance11-Oct-2018
Date of Web Publication23-Nov-2018
 

   Abstract 


Background: Traumatic brain injury (TBI) is the leading cause of mortality, morbidity, and disability globally. Methods for a reliable prediction of outcomes on the admission of TBI cases are of great clinical relevance to stakeholders. Objectives: This study used the International Classification of Diseases-10 codes (S00-S09) for analysis of injury distribution of TBI patients and attempted to find the prognostic predictors of Glasgow coma scale (GCS) in the outcome from readily accessible parameters. Methods: The data were reanalyzed from the Towards Improved Trauma Care Outcomes (TITCO) project from India. TITCO is the prospective, observational, multicenter trauma registry, contained data of trauma patients admitted to four public university hospitals in Mumbai, Delhi, and Kolkata collected from October 2013 to September 2015. Results: Among 8525 cases under study, low GCS scores before admission, which was dependent on the demographic variables and related risk factors occurring at the time of injury, were important in the prognostic predictors of mortality. However, survival probability during hospitalization remained uniformly uncertain for the elderly. Death as outcome of injury was dependent on the average intensity of injury, GCS on admission, critical injury severity score, and intubation within 1 h of admission and between 1 and 24 h of admission. These factors emerged as the independent predictors of fatality. The time of the day of injury did not yield any significant association with low GCS or demise in our study. Conclusions: GCS <8, i.e., severe at the time of admission, was an unfavorable predictor of in-hospital mortality.

Keywords: Glasgow coma scale, injury severity score, outcome assessment, traumatic brain injury

How to cite this article:
Singh M, Pal R, Yarasani P, Bhandarkar P, Munivenkatappa A, Agrawal A. International classification of diseases-based audit of the injury database to understand the injury distribution in patients who have sustained a head injury (International Classification of Diseases Codes: S00-S09). J Emerg Trauma Shock 2018;11:253-64

How to cite this URL:
Singh M, Pal R, Yarasani P, Bhandarkar P, Munivenkatappa A, Agrawal A. International classification of diseases-based audit of the injury database to understand the injury distribution in patients who have sustained a head injury (International Classification of Diseases Codes: S00-S09). J Emerg Trauma Shock [serial online] 2018 [cited 2018 Dec 10];11:253-64. Available from: http://www.onlinejets.org/text.asp?2018/11/4/253/246024





   Introduction Top


Traumatic brain injury (TBI) leads to morbidity, mortality, and disability worldwide with a higher burden in the developing world, where the risk factors of injury occur at higher rates and dedicated interventions are easily available. Different research groups are in search of precise predictors regarding the outcome of a TBI injury case within routine useful resources on admission to hospital. Glasgow coma scale (GCS) is reaching 50 years after introduction in clinical parlance and is still widely used as a convenient tool to predict the outcomes of injury. Further, prognostic markers are valuable tools to support caregivers on early and optimum decisions during critical hours and are useful in research studies to compare the outcomes in different settings. Many prognostic models have been described to provide acceptable insight on outcomes on TBI for real-time expectation of the careseekers, quantify and classify the severity, and evaluate the quality of care.[1]

The likelihood of recovery from unconsciousness in TBI patients depends on the numerous factors, including degree of brain damage, clinical condition on admission, and complications acquired during the clinical interventions to predict neurologic outcomes.[2] Although different research groups have tried to introduce varied indices for prediction of prognosis of TBI cases, extended GCS is accepted as the most-sensitive tool to predict hospital deaths.[3],[4],[5] In the above scenario, the current study attempted to delineate injury patterns and distribution in a cohort of trauma population admitted to the tertiary dedicated trauma care center in search of a critical decision-making process for the best possible outcomes in resource-limited settings of a developing country. The present study was aimed to find the usefulness of the International Classification of Diseases (ICD)-10 codes to understand the injury distribution and the effectiveness of GCS to prognosticate the outcome in TBI patients from its predictors.


   Methods Top


This study explored the use of ICD-10 codes to find the magnitude of injury of TBI victims and to evaluate the predictive accuracy of GCS from the risk factors leading to TBI and fatality and also to investigate whether correlations exist between variables in the acute stage of injury and outcome measures in TBI patients from readily accessible parameters.

The database from the Towards Improved Trauma Care Outcomes (TITCO) project from India was used in this analysis. The data of this multicenter trauma registry generated from four public university hospitals in Mumbai, Delhi, and Kolkata contained data of trauma patients admitted to these hospitals.[6] TITCO data were collected from October 1, 2013, to September 30, 2015. Patients' details of trauma cases were recorded by trained data collectors at each identified center of TITCO. All patients presenting to the casualty department with a history of injury (road traffic, railway, fall, assault, or burns) and who were admitted to the hospital for treatment were included in the study, hereafter called the “injured patient.” Patients who died just after arrival (but before admission) were also included. Patients who were dead on arrival were excluded from the study. Patients who died between arrival and admission were included. The details of TITCO methodology can be found elsewhere.[7]

Data collection procedure

The institutional ethics committee (IEC) approval was obtained before collecting data from all the participating hospitals of multicentric TITCO project and all the ethical approaches were followed to safeguard the participants in this study as per Helsinki Declaration. All the patients with TBI were considered for this study. All the records from the TITCO registry were classified according to ICD-10. In registry, ICD-10 codes to each record were provided on the basis of information collected at various stages, which mainly included while collecting initial data set, on the basis of noninvasive investigations: roentgenography (X-ray), computed tomography (CT), and magnetic resonance imaging.

All the records with at least one code from category “injuries to head (S00-S09)” were considered for this study. ICD codes specified previously to individual record with a head injury were reanalyzed and coded with ICD-10. For multiple ICD-10 codes, alphabetically sorted three digital sequences were arranged for all records. Demographic variables including age and gender, type, mechanism and severity of trauma, transport mode, time of the day, GCS, vital and biochemical parameters at the time of admission and postadmission, and postinjury interventions were the predictors which were considered. Representations of all records according to the ICD-10 codes of head injuries were made with respect to each secondary variables of interest.

Statistical analysis

The ICD-10 codes became effective from October 1, 2015. In this study, the TITCO data were reanalyzed using ICD-10 codes. All the variables under consideration were represented as frequency and percentages of each of relevant each group. Cross-tabulation of qualitative variables was made using Chi-square test, and an alpha level of 5% was considered statistically significant. Data were analyzed using SPSS 24.0 for Windows (IBM Inc., Chicago, IL, USA).


   Results Top


In the current registry of 9192 trauma patients, the data of GCS at the time of admission were available for 8525 only. It was evident that half of the patients (50%) presented with clinical features of dislocation, sprain, and strain of joints and ligaments of the head presented with the lowest category of GCS (<9) denoting as severe. All other fracture and injuries at different anatomical locations resulted in low GCS in more than one-third of the cases. Death was an important outcome observed among 23.9% of all cases and in the majority among those who suffered dislocation, sprain, and strain of joints and ligaments of the head (30.8%). Other unspecified injuries of head also resulted in death in 31.3% of cases [Table 1].
Table 1: Location of head injury as a predictor of severe Glasgow coma scale and death as outcome

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Age was a significant predictor of low GCS as compared to mild and moderate in the bivariate analysis. The young adult age group (21–40 years) and the oldest age group (above 80 years) experienced 35.8% and 46.2% of low GCS after injury, respectively. However, fatality as an unfortunate outcome was observed in majority in the older age group of 61–80 years (39.3%) and above 80 years (35.1%). Males were in majority to experience low GCS as well as death compared to females. A higher number of cases reported during midnight to 3 am in the morning (20.56%) and in the evening, i.e., 3 pm to 6 pm (16.11%) [Table 2].
Table 2: Preadmission predictors of severe Glasgow coma scale and death as an outcome

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Blunt injuries were more associated with lower GCS (31.8%) as compared to penetrating injuries and were significantly related to mortality outcomes deaths (23.9%) as compared to penetrating injuries. Admissions with railway injury led to good numbers of low GCS (54.3%) and fatality (45%). Among road traffic injury (RTI) cases, a higher number of the bicyclists suffered from low GCS (48.7%), with one-thirds (33.3%) having fatal outcomes. Among RTI victims, 47.2% of the pedestrians presented with severe GCS, whereas 40.2% of motorcyclists presented with injury leading to severe GCS. Death was higher among pedestrians (20.5%) compared to motorcyclists (18.9%). Majority of victims, brought by the police, had low GCS (50.8%) and faced mortality (36.5%). In the majority of cases, ambulance was the most common mode of transport to bring to the facilities, and it was the second most common mode through which majority of low GCS and mortality was observed. Around one-fourth (24.5%) of the admitted cases, who were transferred between facilities, resulted in death as the outcome after admission; transfer between facilities showed no statistically significant differences in proportion of severe of GCS [Table 2].

Among the diurnal variables, “time of the day,” “mean of average intensity of injury,” and “time duration between injury and arrival at hospital” were all significantly related with low GCS scores and death after admission in the facility. More than half of the patients with category “critical” injury severity score (ISS) (55.7%) belonged to “severe” GCS category. This proportion of low GCS increased significantly across minor to severe to critical category of ISS (P = 0.00). Similar relation of the distribution of the victims was observed in the cases of mortality data also [Table 2].

The first recorded vital parameters in emergency departments (EDs) of the hospitals had shown notable findings in our study. The mean systolic blood pressure (SBP) was significantly higher among the trauma victims who died (95.14 ± 50.51) after reaching the facility (P = 0.00). The mean respiratory rate, heart rate, and oxygen saturation among patients who died were also significantly lower as compared to those who survived after admission. The mean GCS score among patients who were alive on reaching ED was significantly higher (12.30 ± 3.49) than those who died (7.03 ± 3.82) (P = 0.00) [Table 3].
Table 3: Vital and biochemical parameters and inpatients interventions predicting death

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The blood biochemical parameters recorded at the time of admission showed that the mean hemoglobin and hematocrit levels were significantly higher among those who were alive as compared to deceased cases. Blood glucose and serum creatinine levels were observed to be significantly higher among patients who later died as inpatients compared to those who were alive after intervention. The mean lengths of ventilator support, intensive care unit (ICU) stay, and hospital stay were significantly higher (73.52 ± 150.01 h and 85.48 ± 174.26 h, respectively) among patients who later died (P = 0.00) [Table 3].

Patients who were brought to the hospitals with a ventilator support before arrival had a higher proportion of deaths (62.9%) as compared to those who were intubated within 1 h of arrival (44.7%) (P = 0.00). Surgical airway maintenance within 1 h of admission (53.2%) and before arrival (65.2%) led to higher proportion of survival. Intercostal drain was inserted in 3.5% (n = 342) of patients, and among those who reported with intercostal tube (ICT) before arrival, 55.6% died. Patients who were taken for a surgical procedure in operation theater (OT) within 1 h of arrival had a higher proportion of mortality (33.3%) as compared to those who were not (23.8%), but this distribution was not statistically significant (P = 0.34). As the intubation was delayed (after 1 h and <24 h), the proportion of mortality among them increased (50.5%). A similar trend was observed among those who underwent surgical airway and ICT insertion. However, it was noted that mortality proportion decreased among patients who underwent surgery after 1 h (26.2%) as compared to those who underwent surgery within 1 h [Table 4].
Table 4: Interventions after injury to predict fatal outcome

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The GCS after injury was presumed to be dependent on the demographic variables and factors occurring at the time of injury or before admission. The multinomial regression model identified the predictors of low GCS and their association presented in the form of adjusted odds ratio (OR). Age emerged as an independent predictor of low GCS. The youngest group of patients (0–20 years) in our series contracting injury had 71% less chances of having low GCS as compared to highest age group (>80 years) (OR; 95% confidence interval [CI]: 0.29 [0.17–0.51]); this chance decreased as age increased. Male victims had 37% higher chances of low GCS (OR; 95% CI: 1.37 [1.20–1.50]) [Table 5].
Table 5: Multinomial logistic regression analysis determining the predictors of severe Glasgow coma scale

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The time of day did not yield any significant association with low GCS in our series. However, a positive association was observed between 6 am and 12 am as compared to 9 pm onward till midnight. Railway track injuries had around four times higher risk of low GCS as compared to other mode of injuries (OR; 95% CI: 4.06 [2.69–6.12]). Similarly, the risk of having low GCS remained 2–4 times higher in injured cases from road traffic accidents (RTAs) did not differ whether the victims were drivers or pedestrians. Head injury (only in the form of only one of these types: skull fracture or joint dislocation or intracranial or cranial nerve or open wound or superficial or any unspecified injury) was significantly protective against low GCS with around 22% lower risk (OR; 95% CI: 0.78 [0.69–0.87]) as compared to combination of the above injuries of the head [Table 5].

On applying the multinomial regression model to identify the predictors of mortality as an outcome of injury, “average intensity of injury,” “GCS on admission,” and “critical ISS,” “intubation within 1 h of admission and between 1 and 24 h of admission” emerged as the independent predictors of mortality among the cases registered in our series [Table 6].
Table 6: Multinomial logistic regression to predict mortality of traumatic brain injury

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   Discussion Top


Prediction remains elusive after severe TBI regarding the overall roles of the leading risk factors and risk correlates on the outcomes of death and disability. Hence, innovating reliable predictive elements on admission to direct the expectations of caregivers as well as careseekers is of paramount clinical relevance including the injury patterns as predictors of quality and quantity of outcomes.

Intracranial and extracranial injuries

In our study, low GCS was observed in more than one-third of patients of intracranial injury and around 40% of those with superficial injuries to the head and other unspecified injuries of the head. The combination of various types of head injury had a higher risk of low GCS as compared to single type of injury. Literature reports that intracerebral hematomas occur in one-thirds of patients with moderate-to-severe TBI, and 1% with minor TBI, while diffuse axonal injury occurs in a majority of TBI cases to some degree; low-grade axonal injury is usually microscopic and not detected by CT. Yet, traumatic subarachnoid hemorrhage is one of the most common findings in TBI, occurring in up to 40% of patients with moderate-to-severe TBI, and 5% of patients with minor TBI, while subdural hematomas are the most common type of mass lesion noted in one-fifths of patients with moderate-to-severe TBI, and one-third of fatal TBI; epidural hematoma and contusions are also quite common.[8] Other research group also mentioned that prognosis in TBI depends on the nature and extent of the intracranial injuries (worst to least, subdural more than extradural more than SAH).[5]

Dislocation, sprain, and strain of joints and ligaments of the head

In the current registry, low GCS (3–8) was observed in half of the TBI cases with dislocation, sprain, and strain of joints and ligaments of the head, more than one-third among all other fractures and injuries of different anatomical locations. Fatality was high as overall outcomes (23.9%); majority (30.8%) among those with dislocation, sprain, and strain of joints and ligaments of the head; unspecified injuries of the head also resulted in high death (31.3%). Literature reports that most of the TBI cases have a combination of injuries. Skull fractures occur in about 5% of patients with mild TBI and up to half of those with severe TBI.[8]

Age

In our series, age emerged as an independent predictor of low GCS after adjusting for all possible confounders. The youngest group of patients (0–20 years) had 71% less chances of having low GCS as compared to the highest age group (>80 years); the overall chance of survival decreased as age increased. Other research groups also noted that elderly patients (aged 60 years and older) have an increased mortality after isolated TBI. Hence, aggressive intervention of elderly TBI cases is warranted, and efforts should be made to decrease in-patient mortality.[9] A study from Switzerland reported that age ≤40 years was a strong predictor of favorable outcome at follow-up supported by other researchers also.[5],[10]

Gender

In our study, TBI victims with low GCS were males in majority of cases and suffered more death compared to females. Published literature reported by other research groups noted that males were overrepresented by 3:1 in all subgroups of TBI.[8]

Time

The time of day did not yield any significant association with low GCS or subsequent deaths among the trauma cases in our series. However, a positive association was observed between 6 am and 9 am as compared to 9 pm onward to midnight though a higher numbers of cases were reported during midnight to late nights also. However, the prehospital time lapse before arrival to definitive care facilities increased both GCS and mortality.

Mechanism of injury

It was observed in our study that reported railway track injuries led to four times higher risk of low GCS as compared to other mode of injuries; the risk of low GCS in RTI remained 2–4 times higher than others with no perceived difference between driver or pedestrian though the fatalities were huge among bicyclists (33.3%), pedestrians (20.5%), and motorcyclists (18.9%). In the study by Mosenthal et al., the mechanisms of injury were falls (34%), assaults (28%), motor vehicle collisions (14%), pedestrian (11%), and other (12%). Falls were more common in the older patients and assaults in the younger group.[8] Pedestrians accounted for the majority of RTA victims in reported studies conducted in Ethiopia, Tanzania, and Nigeria.[11],[12],[13]

Mode of transport

Majority of victims in our series who were brought by the police, in any type of vehicle, were reported to have low GCS score (50.8%) and moderate mortality (36.5%). Ambulance was the second most common mode of transport through which majority of low GCS and mortality was carried to the definitive care centers. However, the mode of transport in public transport and private vehicles demonstrated a lower risk of severe GCS. The probable reason could be reduced time duration in receiving early intervention as compared to other modes of transport for which the patient has to wait. Roy et al. in their survey at urban trauma center also reported that a fifth of the casualties came by a government ambulance (21.4%), and these were all interhospital transfers with an accompanying doctor or nurse. They have also highlighted that ambulance services are being run by a multitude of agencies that include the government, police, fire brigades, hospitals, and private agencies. In majority of cases, unskilled labor handle the most specialized tasks.[14]

Transferred (interfacility)

Around one-fourths of TBI cases in this series were transferred between facilities, and the positive association was observed between interfacility transfer and death among patients. Yet, no statistically significant difference in proportion of severity of GCS or death as outcome was noted for this interfacility transfer. Inter-facility transfer was done only when the cases could not be managed in less than required capacity compared to severity of case, lead to higher chances of mortality. The mean intensity of injury of TBI cases had significant positive association with low GCS and negative association with death after admission in the facility.

Time duration between injury and arrival at hospital

The mean time duration between injury and arrival at hospital was significantly higher among those who died after admission in the facility. In a retrospective analysis by an ambulance dispatch center in Urmia, Iran, around eight out of ten victims in their city were transported in <30 min, while for victims on interurban roads, one out of four had been transported in <30 min.[15] Our study measured the time from the event to the arrival at the study center, and in the cases of low GCS and death as outcome, the mean time was >30 min. It also includes the time from the reported event to first care received. However, studies have revealed that rapid responses had a major effect on the quality of care provided to trauma patients. Planners of emergency medical service need to focus in providing services to victims in all types of geographical locations.[16],[17] The time intervals also need splitting up to improve the understanding of the precious “golden hour.”

Injury severity score tag

More than half of the TBI cases in our series with category “critical” ISS (55.7%) belonged to “severe” GCS category. This proportion of low GCS increased significantly across minor to severe to “critical” category of ISS with comparable relations of the distribution of TBI victims in the cases of mortality data also. Other research groups, however, observed that there are shortcomings of ISS to improve quality of definitive intra-facility care as assessments differentiated severe injury from the mismanagement of injury for example ISS mixed the outcome of data with injury severity, ISS incorrectly assigned increased severity to the lesser injuries of mismanaged patients.[18]

Vitals recorded after reaching facility

The mean respiratory rate, heart rate, and oxygen saturation were significantly lower among fatal cases, and the mean GCS scores were significantly higher among survived cases than those who died. Many researchers had reported that hypotension was an important risk factor for prognosis of TBI cases.[5],[19],[20] In 1-year analysis on trauma cases at a teaching hospital of Kenya, the mean BP at the time of admission did not yield any significant association with mortality.[21] The mean SBP was significantly higher among those who died in our series, but did not appear to be the independent predictors of death. This finding suggests that not only hypotension but also the presence of hypertension can be associated with poor outcome in patients with TBI.[22],[23] Kanter et al.[22] had suggested that it may be due to stress-induced neurogenic or systemic factors causing a hyperdynamic or vasoconstricted circulatory state.

Mean length of ventilator support, intensive care unit stay, and hospital stay

In our series, the mean lengths of ventilator support, ICU stay, and hospital stay were significantly higher among TBI cases who later died during their hospital stay. Saidi et al. from Kenya also reported that head involvement, ICU admission, nonsurgical interventions, and blood transfusion were significantly associated with mortality.[21] Other research groups also reported that probability of recovering consciousness in TBI patients depended on several other factors, including extent of brain damage, clinical condition on admission, and complications developed during the ICU stay[2] and hypoxia.[3] On analysis of motor vehicle accidents in Guinea, the mean reported time interval between accident and hospital arrival was predicted as an important determinant of length of hospitalization and of in-hospital mortality.[24] We hypothesized that the length of stay and duration between injury and arrival were important predictors of death; however, the regression model did not yield these two variables as the independent predictors.

Postadmission interventions

Patients in our series, who were brought to the hospital with a ventilator support before arrival, had a significantly higher risk of death compared to intubation of cases within 1 h of arrival. Those ventilated before being admitted to our centers were probably those patients who were transferred between facilities due to their higher chances of death. Surgical airway maintenance within 1 h of admission (53.2%) and before arrival (65.2%) increased the probability of survival while “intercostal drain” or “taking to OT for a surgical procedure within 1 h of arrival” could not show significant survival benefits. Delay in intubation (after 1 h and <24 h) increased the risk of death significantly by five times.

Predictors of low Glasgow coma scale and mortality

In our series, on applying the multinomial regression model to identify the predictors of mortality as the outcome of injury, average intensity of injury, GCS on admission, critical ISS, and intubation within 1 h of admission and within 1–24 h of admission emerged as the independent predictors among the cases registered.

Other research groups were also of the opinion that information about the cumulative research findings on the predictive ability of risk factor on the GCS scores aided nurses in providing support and education to family members during the acute stage of injury and in coordinating the services of members of the health-care team, which could result in improved outcomes for both patient and family. In a systematic review, prospectively collected individual patient data were analyzed from 11 studies where they noted as the strongest predictors of outcome were age, motor score, pupillary reactivity, and CT characteristics, including the presence of traumatic subarachnoid hemorrhage. A prognostic model proposed a combination of age, motor score, and pupillary reactivity; the model could have been improved by considering CT characteristics, secondary insults (hypotension and hypoxia), and abnormal laboratory parameters (glucose and hemoglobin).[1]

Strengths and limitations

Prediction of death and functional outcome is essential for determining treatment strategies and allocation of resources for patients with severe TBI. Yet, we had several limitations. First, the impact of GCS score and pupil parameters on mortality rate and outcome in severe TBI cases could have been added to our study protocol for better prognostic functions. Further, functional and quality of life as well as patient experience and indicators such as return to work, education, and social dependency could also be added when broad outcome categories were used.


   Conclusions Top


GCS <8, i.e., severe at the time of admission of TBI cases, was an unfavorable predictor of in-hospital mortality. Early diagnosis and prompt interventions are needed in search of predictive risk factors for severe TBI to determine the biobehavioral intervention strategies and allocating resources to reduce death from TBI. We need multiprofessional, multidisciplinary expert research group for evidence-based outcome measures with incorporation time-motion components for TBI cases, especially of extreme of ages who could have been benefitted from early and aggressive treatment inside the dedicated facilities on admission.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.



 
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Correspondence Address:
Dr. Amit Agrawal
Department of Neurosurgery, Narayana Medical College Hospital, Chinthareddypalem, Nellore - 524 003, Andhra Pradesh
India
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Source of Support: None, Conflict of Interest: None


DOI: 10.4103/JETS.JETS_90_17

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    Tables

  [Table 1], [Table 2], [Table 3], [Table 4], [Table 5], [Table 6]



 

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