India is undergoing a rapid epidemiological transition as a consequence of social and economic changes, yet wide disparities in mortality persist across its various regions and states. These variations reflect inequalities in access to essential resources such as nutrition, safe drinking water, sanitation, education, medical care, and other basic human needs. Additional differences arise from variations in risk factors, behavioural patterns, and socio-cultural contexts in different parts of the country. Globally, medically certified information is available for less than 30% of the estimated 50.5 million annual deaths, and in developing countries, including India, the causes of childhood mortality are often poorly documented 1,2. India’s diverse geography, lifestyle patterns, and socio-economic status contribute to heterogeneity in child mortality rates. Progress in child survival varies across age groups; global reductions in the under-5 mortality rate (U5MR) have been greater among post-neonatal children than among neonates (0–28 days) 3. At the local level, it remains unclear how these patterns are shifting, posing challenges to the Sustainable Development Goal (SDG) 3.2, which aims to “end preventable deaths of newborns and children under 5” by 2030, with targets to reduce neonatal mortality to ≤12 per 1000 live births and U5MR to ≤25 per 1000 live births 3. Reliable mortality data are essential for health planning, resource allocation, and evaluation of healthcare quality. However, although mortality data are routinely recorded in hospital medical record departments, they are often not systematically analysed into actionable information 4–6. Hospital-based death records are valuable for identifying major causes of illness and mortality, case fatality rates, age and sex distribution, and care-seeking patterns within communities 7,8. This information is vital for prioritising public health programmes, improving healthcare delivery, and addressing disparities. Sex-based differences in child mortality present an additional challenge. Historical data indicate excess female child mortality in parts of Europe and North America until the early 20th century^9^. In South-Central Asia, including India, excess female mortality persists, resulting in an estimated 250,000 preventable deaths among girls under five annually 10. This inequity undermines gender equality goals and hampers further reductions in child mortality. Despite national declines in mortality over recent decades, significant variation exists between regions in India regarding the predominant causes of child death 1,6,11. Mortality patterns serve as key indicators of community health, guiding both preventive strategies and clinical interventions. The present study was undertaken to analyse the disease-specific mortality patterns and outcomes of critically ill children admitted to the Paediatric Intensive Care Unit (PICU) of a tertiary care hospital in South India, thereby contributing to the body of epidemiological evidence needed for informed health policy and practice. AIM & OBJECTIVES: 1. To know the disease specific mortality pattern of all critically ill children aged 1 month to 18 years admitted to the paediatric intensive care unit of Department of Paediatrics, Kurnool Medical College, Kurnool 2. To study the outcome of all critical ill children admitted to paediatric intensive care unit with different diseases.

Study Setting: Paediatric Intensive care unit, Government general hospital, Kurnool medical college, Kurnool. Study Design: Retrospective study Study Population: All critically ill children aged 1month to 18 years admitted to the Paediatric intensive care unit, GGH, Kurnool medical college and hospital, Kurnool. Study period: 1year. INCLUSION CRITERIA: All critically ill children aged 1month to 18 years admitted to the Paediatric intensive care unit, GGH, Kurnool Medical College, Kurnool with different diseases. EXCLUSION CRITERIA: 1. Children below 1 month admitted in the department of paediatrics, Kurnool Medical College, Kurnool. 2. Children with associated major congenital anomalies. DATA COLLECTION PROCEDURE: The data will be collected from the medical records of the study institution. The study variables included will be age, sex, and details of diagnosis including primary disease, co morbidities and cause of death. STATISTICAL ANALYSIS: 1.The data collected in Microsoft Excel will be analyzed using IBM Statistical Package for Social Science (SPSS version 21). 2. Data related to different variables such as age, sex, cause of mortality, etc. will be retrieved from the registers maintained in the department. The summary statistics for categorical variables are reported using frequency and percentage and continuous variables as mean (SD). Data will be analysed using frequency, proportion and Z-test.

Table 1: Gender distribution in the study population Gender Frequency Percent Male 1633 59.6 Female 1106 40.4 Total 2739 100.0 In the present study, males (59.6%) outnumbered females (40.4%), with a male-to-female ratio of approximately 1.48:1. Table 2: Outcome in the study population Outcome Frequency Percent Death 171 6.2 Survived 2568 93.8 Total 2739 100.0 In the present study, 93.8% of participants survived, while 6.2% (171 individuals) died during the study period. Table 3: Age Vs gender association in the study population Sex N Mean Std. Deviation Std. Error Mean t Test P Value Age in Years Male 1633 4.85 3.97 0.10 1.052 0.293 Female 1106 4.69 4.09 0.12 Males have a slightly higher mean age (4.85 years) compared to females (4.69 years). Since the p-value (0.293) is greater than 0.05, the difference in mean age between males and females is not statistically significant. There is no evidence to suggest that mean age differs by sex in this sample. Table 4: Association Between age and Survival Outcome among Study Participants Outcome N Mean Std. Deviation Std. Error Mean t Test P Value Age in Years Death 171 4.42 4.53 0.35 1.238 0.216 Survived 2568 4.81 3.98 0.08 Those who died had a mean age of 4.42 years, while survivors had a slightly higher mean age of 4.81 years. Since p > 0.05, the difference in mean age between outcome groups is not statistically significant. Age does not appear to be a significant factor associated with survival status in this sample. Table 5: Association between Sex and Survival Outcome Among Study Participants Outcome Total Death Survived Sex Male Count 90 1543 1633 % 5.5% 94.5% 100.0% Female Count 81 1025 1106 % 7.3% 92.7% 100.0% Total Count 171 2568 2739 % 6.2% 93.8% 100.0% Pearson Chi-Square = 3.39700 P Value = 0.0654 Mortality rates were 5.5% in males and 7.3% in females, while survival rates were 94.5% and 92.7%, respectively. Although the mortality rate appears slightly higher among females, the difference was not statistically significant (Pearson Chi-Square = 3.397, p = 0.0654; p > 0.05). This means that, sex was not significantly associated with survival outcome. Table 6: Comparison of Duration of Stay (DOS) and Age by Sex Among Study Participants Sex N Mean Std. Deviation Std. Error Mean t Test P Value DOS Male 1633 5.1715 2.37713 .05882 .213 .831 Female 1106 5.1510 2.60000 .07818 Age in Months Male 1633 58.4085 47.41720 1.17339 1.113 .266 Female 1106 56.3282 48.85929 1.46916 Duration of Stay (DOS): Males had a mean DOS of 5.17 days (SD = 2.38), while females had a mean DOS of 5.15 days (SD = 2.60). The difference was very small and statistically not significant (t = 0.213, p = 0.831). Age (in months): Males had a mean age of 58.41 months (SD = 47.42) compared to 56.33 months (SD = 48.86) for females. The difference was also not statistically significant (t = 1.113, p = 0.266). There were no significant differences between males and females in either duration of stay or age. Table 7: Comparison of Duration of Stay and Age by Survival Status Among Study Participants Status N Mean Std. Deviation Std. Error Mean t Test P Value DOS Death 171 4.6374 3.64088 .27843 2.88 0.004 Survived 2568 5.1982 2.36749 .04672 Age in Months Death 171 53.7251 53.88256 4.12050 1.081 0.28 Survived 2568 57.8244 47.59039 .93912 Duration of Stay (DOS): Mean DOS was 4.64 days for deaths and 5.20 days for survivors. The difference was statistically significant (t = 2.88, p = 0.004), indicating that survivors had a slightly longer hospital stay than those who died. Age (in months): Mean age was 53.73 months for deaths and 57.82 months for survivors. The difference was not statistically significant (t = 1.081, p = 0.28). Only duration of stay showed a statistically significant association with outcome — survivors stayed longer than those who died. Age was not significantly related to outcome. Table 8: Correlation Between Outcome and Selected Variables (Duration of Stay, Age, and Sex) Correlations DOS Age in Months Sex Outcome Outcome Pearson Correlation .055** .021 -.037 1 P Value .004 .280 .054 N 2739 2739 2739 2739 DOS had a very weak positive correlation with outcome (r = 0.055, p = 0.004), which is statistically significant. This suggests that slightly longer stays were associated with survival, though the effect size is minimal. Age had a negligible positive correlation with outcome (r = 0.021, p = 0.280), not statistically significant. Sex had a very weak negative correlation with outcome (r = -0.037, p = 0.054), also not statistically significant. Only duration of stay showed a statistically significant correlation with outcome, and even that relationship was very weak. Table 9: Distribution of Cases by System Involved Among Study Participants System No. of Cases % of Cases Endocrine 20 1% GIT 305 11% Haematology 51 2% Infectious disease 309 11% Poisoning 56 2% Renal system 553 20% Respiratory system 309 11% Blood 13 0% CNS 581 21% CVS 54 2% GE 82 3% Haematology 102 4% Others 266 10% Poisoning 38 1% Total 2739 100% The Central Nervous System (CNS) disorders were the most common, accounting for 21% (581 cases). Renal system disorders were the second most frequent (20%, 553 cases). Other high-frequency categories include Gastrointestinal tract (GIT) (11%), Infectious diseases (11%), and Respiratory system disorders (11%). Less common categories included Poisoning (1–2%), Haematology (2–4%), Cardiovascular system (CVS) (2%), and Endocrine (1%). Rare categories such as Blood disorders accounted for only 0.5% (13 cases). The "Others" category accounted for 10% of cases. Table 10: Major Diagnoses with Corresponding Mortality Rates Among Study Participants Diagnosis N Deaths Survivors Death Rate Respiratory infection (Pneumonia/LRTI) 619 59 560 35% Seizure disorders (incl. febrile/status) 456 24 432 14% Gastroenteritis/Diarrhoeal disease 314 9 305 5% Dengue (all variants) 267 6 261 4% Hematologic disorders 196 24 172 14% Fever/febrile illness (NOS) 182 1 181 1% Poisoning/Envenomation 132 5 127 3% Asthma/Reactive airway 112 4 108 2% CNS infection (Meningitis/Encephalitis) 101 22 79 13% Renal/UTI/Glomerular disease 101 5 96 3% Bronchiolitis 91 4 87 2% Liver disease/Hepatitis 83 2 81 1% Malnutrition/FTT 28 1 27 1% Others 56 5 51 3% The highest case volume was for Respiratory infections (Pneumonia/LRTI) with 619 cases and the highest death count (59 deaths), giving a mortality rate of 35%, the highest in the dataset. Seizure disorders (456 cases) and Hematologic disorders (196 cases) both had notable mortality rates of 14%. CNS infections (Meningitis/Encephalitis) had 101 cases with a high mortality rate of 13%. Other common conditions such as Gastroenteritis/Diarrhoeal disease (5%), Dengue (4%), and Poisoning/Envenomation (3%) showed lower death rates. Conditions like Fever (NOS), Liver disease, and Malnutrition had very low mortality rates (1%). The "Others" category accounted for 56 cases with a mortality rate of 3%. Table 11: Age-Wise Distribution of Major Diagnoses and Mortality Rates Among Pediatric Patients Diagnosis Infants 1-5 Years >5 Years Deaths Survivors N Infants 1-5 Years >5 Years Deaths Respiratory infection (Pneumonia/LRTI) 25 20 14 59 560 619 38% 31% 34% 35% Seizure disorders (incl. febrile/status) 10 8 6 24 432 456 15% 12% 15% 14% Gastroenteritis/Diarrhoeal disease 6 2 1 9 305 314 9% 3% 2% 5% Dengue (all variants) 2 3 1 6 261 267 3% 5% 2% 4% Hematologic disorders 5 12 7 24 172 196 8% 18% 17% 14% Fever/febrile illness (NOS) 0 1 0 1 181 182 0% 2% 0% 1% Poisoning/Envenomation 0 3 2 5 127 132 0% 5% 5% 3% Asthma/Reactive airway 1 2 1 4 108 112 2% 3% 2% 2% CNS infection (Meningitis/Encephalitis) 7 9 6 22 79 101 11% 14% 15% 13% Renal/UTI/Glomerular disease 2 1 2 5 96 101 3% 2% 5% 3% Bronchiolitis 3 1 0 4 87 91 5% 2% 0% 2% Liver disease/Hepatitis 1 1 0 2 81 83 2% 2% 0% 1% Malnutrition/FTT 1 0 0 1 27 28 2% 0% 0% 1% Others 2 2 1 5 51 56 3% 3% 2% 3% Respiratory infections (Pneumonia/LRTI) were the leading cause of both morbidity (619 cases) and mortality (59 deaths). Mortality was fairly distributed across infants (38%), 1–5 years (31%), and >5 years (34%). Seizure disorders (456 cases, 24 deaths) contributed significantly to mortality, with similar proportions across age groups (12–15%). Hematologic disorders (196 cases, 24 deaths) had a high death rate (14%), particularly in the 1–5 years group (18%) and >5 years (17%). CNS infections (101 cases, 22 deaths) also showed high mortality (13% overall) with consistent burden across age groups (11–15%). Other common conditions like Gastroenteritis (5%), Dengue (4%), Poisoning (3%), and renal disease (3%) showed moderate to low mortality. Conditions such as Fever (NOS), Liver disease, and Malnutrition had very low mortality (≤1%). Mortality in Bronchiolitis (2%) occurred mainly in infants (5%), with none in >5 years. Respiratory infections, seizure disorders, hematologic disorders, and CNS infections are the leading contributors to mortality, cutting across all pediatric age groups. Infants and >5 years age groups bore slightly higher mortality for several conditions, highlighting vulnerability at both extremes. Table 12: Monthly Survival Rates, Mortality Rates, and Average Duration of Stay Among Study Participants Months Survived Survived(%) Died Died (%) Average DOS 1 342 95% 19 5% 4.751 2 288 97% 10 3% 6.178 4 251 93% 19 7% 6.281 5 322 93% 24 7% 5.835 7 230 93% 18 7% 5.218 8 278 93% 21 7% 4.201 9 295 92% 25 8% 4.931 10 279 94% 19 6% 4.319 12 283 95% 16 5% 4.870 Survival rates across months ranged from 92% to 97%, with the highest in month 2 (97%) and the lowest in month 9 (92%). Mortality rates ranged from 3% to 8%, with month 2 showing the lowest mortality (3%) and month 9 the highest (8%). Average DOS varied from 4.20 days (month 8) to 6.28 days (month 4). There is no consistent trend linking survival percentage with DOS; for example, month 2 had the highest survival but also one of the higher average DOS values, while month 8 had lower DOS but average survival rates. Survival remained relatively high (>90%) across all months, mortality fluctuated within a narrow range, and average hospital stay showed modest variation without a clear temporal pattern. AUC 61.7 Cut-off 0.910579 Sensitivity 0.907 Specificity 0.69 Youden Index 0.597 LR+ 2.926 LR- 0.135 PPV 0.745 NPV 0.881 Interpretation of ROC and Diagnostic Performance The Area Under the Curve (AUC) is 61.7%, which shows a fair ability of the model to differentiate between survival and non-survival outcomes. The optimal cut-off value identified is 0.910579, selected based on the maximum Youden Index (0.597). At this threshold: Sensitivity: 0.907, The model correctly identifies about 90.7% of the actual survivors. Specificity: 0.690, The model correctly identifies about 69.0% of the non-survivors. The Positive Predictive Value (PPV) is 0.745, meaning that 74.5% of those predicted to survive actually did survive. The Negative Predictive Value (NPV) is 0.881, meaning that 88.1% of those predicted not to survive actually did not survive. The Positive Likelihood Ratio (LR⁺) is 2.926, indicating that patients above the cut-off are about 2.93 times more likely to survive than those below it. The Negative Likelihood Ratio (LR⁻) is 0.135, indicating that patients below the cut-off are only 13.5% as likely to survive compared to those above it.

The present study provides important insights into the demographic profile, hospital stay characteristics, and survival outcomes among the study population. By analyzing variables such as gender, age, duration of stay, and monthly trends, as well as evaluating predictive model performance, the findings help identify patterns that can inform both clinical decision-making and public health strategies. In this section, the results are critically examined in light of existing literature, highlighting consistencies, divergences, and the potential implications for patient management and healthcare planning. Gender Distribution and Outcome Patterns In the present study, males comprised 59.6% of the sample, with a male-to-female ratio of approximately 1.48:1. This male predominance is consistent with findings from hospital-based mortality and morbidity surveys in India, where higher male admission rates are often attributed to gendered health-seeking behaviors, occupational exposures, and sociocultural factors that prioritize treatment for male patients (12,13). Mortality was slightly higher among females (7.3%) compared to males (5.5%), though this difference was not statistically significant (p = 0.0654). Several studies have reported mixed findings regarding gender differences in mortality, with some showing higher male mortality due to risk-prone behaviors and comorbidities (14), while others, similar to our results, find no statistically significant association (15,16). The lack of significance here may reflect similar access to care and management protocols across genders in the study setting. Duration of Stay (DOS) and Survival The mean hospital stay did not differ significantly between males and females (p = 0.831). However, when comparing survivors and non-survivors, survivors had a significantly longer mean stay (5.20 days) than those who died (4.64 days, p = 0.004). This inverse relationship between shorter stays and higher mortality has been noted in other hospital-based studies, where early deaths often occur due to severe baseline conditions or delayed presentation (17,18). Interestingly, duration of stay showed a weak but statistically significant positive correlation with survival (r = 0.055, p = 0.004). While the effect size was minimal, it suggests that extended hospitalization might allow for more effective monitoring and treatment, potentially improving outcomes. This aligns with observations in pediatric and critical care studies where prolonged stabilization periods improve discharge survival rates (19). Age and Outcome Association The mean age of participants was around 57 months, with no significant difference between survivors and non-survivors (p = 0.28). The absence of an age-outcome association may be due to the relatively narrow pediatric age focus of the study, reducing the influence of age-related physiological vulnerabilities seen in neonatal or adolescent extremes (20). This contrasts with literature indicating that age is a strong mortality predictor in broader pediatric cohorts, particularly among infants (21). Monthly Trends and Temporal Patterns Monthly survival rates ranged from 92% to 97%, with minimal fluctuation. Mortality was highest in September (8%) and lowest in February (3%), but no consistent seasonal trend emerged. Similar observations have been made in hospital datasets where mortality peaks align variably with local epidemiological patterns of infectious diseases, climatic conditions, or admission surges (22). Average DOS varied from 4.20 to 6.28 days, without clear correlation to survival percentages. The lack of temporal association may suggest that monthly differences in hospital load or case mix had limited impact on overall survival outcomes, possibly reflecting consistent standards of care across the year. Predictive Model Performance (ROC Analysis) The ROC curve analysis yielded an AUC of 61.7%, indicating fair discriminatory ability for predicting survival outcomes. Sensitivity (90.7%) was high, meaning most survivors were correctly identified, but specificity (69.0%) was lower, leading to some misclassification of non-survivors. In clinical decision-making, this balance suggests the model is more effective at ruling in survival than accurately predicting death, a pattern commonly seen in hospital mortality prediction models that prioritize sensitivity to avoid missing potential survivors (23,24). The positive likelihood ratio (2.926) indicates that patients above the identified cut-off are nearly three times as likely to survive, while the negative likelihood ratio (0.135) highlights that those below the threshold have a markedly reduced probability of survival. This performance level is comparable to other moderate-accuracy predictive models in pediatric inpatient care (25). System-wise Distribution of Cases: The present study found that Central Nervous System (CNS) disorders (21%) and renal system disorders (20%) were the most common categories of admission, followed by gastrointestinal tract (GIT), infectious diseases, and respiratory system disorders (each ~11%). This distribution is consistent with findings from other tertiary care PICUs in India, where CNS pathologies, including seizure disorders, meningitis, and encephalitis, are among the leading causes of critical illness 26,27. Renal system disorders, often due to acute kidney injury secondary to systemic illnesses, also contribute significantly to pediatric critical care admissions 28. Infectious diseases—both systemic and organ-specific—remain a major contributor, highlighting the continued burden of communicable diseases in the pediatric age group despite advances in vaccination and public health interventions 29. The relatively lower proportion of endocrine, cardiovascular, and hematologic disorders in our cohort mirrors earlier multicentric observations, which report these as less frequent but often associated with higher resource utilization when present 30. Diagnosis-specific Mortality Patterns: Respiratory infections (pneumonia/LRTI) were the leading cause of death in our study, with a mortality rate of 35%. This aligns with global estimates identifying pneumonia as a leading cause of childhood mortality, particularly in low- and middle-income countries 31. High mortality in respiratory infections may be attributed to late presentation, severe hypoxemia, and limited availability of advanced respiratory support in some settings 32. Seizure disorders and hematologic disorders both demonstrated notable mortality rates (14%), reflecting the severity of underlying neurological insults and complications such as refractory seizures or severe anemia 33. CNS infections (meningitis/encephalitis) showed a mortality rate of 13%, comparable to other Indian hospital-based studies reporting mortality between 10–20% for these conditions 34. On the other hand, conditions such as gastroenteritis, dengue, poisoning, and renal disorders exhibited lower mortality rates (3–5%), possibly reflecting early recognition and effective supportive management 35,36. Very low mortality in febrile illness (NOS), liver disease, and malnutrition may indicate either milder forms of these conditions in admitted cases or effective inpatient interventions. The high mortality burden from preventable and treatable conditions like pneumonia, seizures, and CNS infections underscores the importance of timely referral, availability of intensive care resources, and strengthening primary care to manage these illnesses before progression to critical states 37. Age-specific and Temporal Trends: Mortality patterns varied by age, with infants (<1 year) contributing the highest proportion of deaths, consistent with the vulnerability of this group to infectious diseases, respiratory illnesses, and congenital disorders 38. Children aged 1–5 years demonstrated relatively lower mortality, while mortality again increased in children over 5 years, likely reflecting late presentation of systemic illnesses or trauma-related complications, as noted in earlier reports 39. Monthly survival patterns showed fluctuations but without a consistent seasonal trend. Mortality peaks were noted in the monsoon months, possibly related to seasonal surges in respiratory and vector-borne infections, as documented in similar Indian settings 40. Clinical and Public Health Implications The findings underscore the importance of early, intensive management for high-risk patients, particularly those presenting with severe conditions leading to shorter stays. While gender and age were not significant predictors, monitoring hospitalization duration trends could serve as a practical indicator for patient outcome risk. Predictive models, even with moderate accuracy, may have utility as screening tools to flag high-risk cases for enhanced monitoring, provided they are integrated with clinical judgment.

This study highlights the burden of disease-specific mortality among critically ill children admitted to a tertiary care PICU in South India. CNS and renal disorders were the leading causes of admission, while respiratory infections, seizure disorders, hematological illnesses, and CNS infections contributed most to mortality. Infants remain the most vulnerable age group, with mortality disproportionately higher than in older children. Although monthly variations were observed, no consistent seasonal trend emerged, suggesting that case severity rather than seasonal load drove outcomes. The predictive model demonstrated fair performance, with high sensitivity but modest specificity, indicating potential utility in risk stratification but underscoring the need for refinement with more robust clinical predictors. Importantly, many of the leading causes of mortality—such as pneumonia, seizures, and CNS infections—are preventable and treatable conditions. Strengthening early recognition, referral systems, and critical care infrastructure can therefore yield substantial survival benefits. The findings underscore the need for ongoing investment in pediatric intensive care, integration of predictive tools into clinical decision-making, and focused public health interventions to address preventable causes of childhood mortality. Multicentric studies incorporating larger datasets and detailed clinical parameters will be essential for developing more accurate, locally relevant risk prediction models to guide care and improve survival outcomes.

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