Comparison of PIRO, APACHE IV, and SOFA Scores in Predicting Outcome in Patients with Sepsis Admitted to Intensive Care Unit: A Two-year Cross-sectional Study at Rural Teaching Hospital

Acharya, Sourya; Agrawal, Shruti; Kumar, Sunil; Dronamraju, Sameera; Gaidhane, Shilpa; Wanjari, Anil; Jaiswal, Praraj; Bawiskar, Nipun

doi:10.5005/jp-journals-10071-24323

Cited by 5 publications

(5 citation statements)

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“…Patients transferred to the MICU from outside the hospital, patients with clear no sepsis diagnoses (e.g., trauma, myocardial infarction, and pulmonary embolism), post-surgical patients, and patients not willing to give informed consent were excluded from the study. The Sepsis-3 criteria include two components: “Suspected (or documented) infection and an acute increase in ≥2 sepsis-related organ failure assessment (SOFA) points” and “Suspected (or documented) infection plus vasopressor therapy needed to maintain mean arterial pressure at ≥65 mmHg and serum lactate >2.0 mmol/L despite adequate fluid resuscitation.” The SOFA score is a 24-point measure of organ dysfunction, assessing six organ systems (renal, cardiovascular, pulmonary, hepatic, neurologic, and hematologic), with 0-4 points assigned per organ system [ 6 ].…”

Section: Methodsmentioning

confidence: 99%

“…Mean platelet volume (MPV) is a platelet index that has been available since the 1970s [ 6 ]. Other platelet indices, including platelet distribution width (PDW) and plateletcrit (PCT), have been added since then.…”

Section: Introductionmentioning

confidence: 99%

“…This study had been planned to assess the impact of platelet indices as prognostic markers of sepsis-like morbidity, ICU stay, mortality, and the cause of sepsis (Gram-positive or Gram-negative infections). The SOFA score is a 24-point measure of organ dysfunction, assessing six organ systems (renal, cardiovascular, pulmonary, hepatic, neurologic, and hematologic), with 0-4 points assigned per organ system [6].…”

Section: Introductionmentioning

confidence: 99%

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Utility of Platelet Indices as Prognostic Markers of Sepsis: A Medical Intensive Care Unit-Based Cross-Sectional Study at a Rural Setup

Gupta,

Kumar,

Acharya

et al. 2024

Cureus

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Introduction Even after the breakthrough advancements in the management and prognostic scoring of sepsis, it remains an important cause of morbidity and mortality encountered in intensive care units (ICUs) throughout the globe. This study highlights the utility of platelet indices as prognostic markers of sepsis. Methods In the present prospective cross-sectional study, a total of 177 patients with sepsis were enrolled using the Sepsis-3 criteria. The platelet indices were then linked to severity using the Acute Physiology and Chronic Health Evaluation (APACHE) II score. The correlation of platelet indices to morbidity in terms of the length of ICU stay, need for a mechanical ventilator, types of infection, and mortality was also assessed. Results The results showed that mean platelet volume (p = 0.004) and platelet distribution width (PDW; p = 0.009) were positively correlated with the severity of sepsis. Among all the parameters, plateletcrit (%) was the best predictor of the need for an invasive mechanical ventilator at a cutoff point of ≤0.22 with a 60.90% chance of correctly predicting the need for an invasive mechanical ventilator, as was mortality at a cutoff point of ≤0.22 with a 67.30% chance of correct prediction. Among the platelet indices, only PDW showed a significant association with growth in culture because patients with growth had significantly higher PDW as compared to those who did not have growth (22.4 ± 4.47 vs 20.81 ± 4.29, p = 0.011). Conclusion The difference between the survivors and non-survivor groups was statistically significant for platelet indices, making them easily available, cost-effective, and useful prognostic markers for patients in septic shock. This will help in easy understanding and preventing its morbid complications, even at the primary care physician level.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Utility of Platelet Indices as Prognostic Markers of Sepsis: A Medical Intensive Care Unit-Based Cross-Sectional Study at a Rural Setup

Gupta,

Kumar,

Acharya

et al. 2024

Cureus

Self Cite

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“…This significance arises from its dual role in not only facilitating the evaluation of disease severity but also in enhancing treatment strategies, mitigating unfavorable outcomes, and ultimately prolonging patients' lives. Currently, various clinical scoring systems, such as the Sequential Organ Failure Assessment (SOFA) score [ 5 ], the Acute Physiology and Chronic Health Evaluation (APACHE-II) score [ 6 ], and the Predisposition, Insult/Infection, Response, and Organ Dysfunction (PIRO) score [ 7 ], can assist clinical practitioners in evaluating patients' overall risk and prognosis. Nonetheless, these scoring systems are designed for a broader range of critically ill patients and are not specifically tailored to sepsis.…”

Section: Introductionmentioning

confidence: 99%

A time series driven model for early sepsis prediction based on transformer module

Tang,

Zhang,

2024

BMC Med Res Methodol

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Sepsis remains a critical concern in intensive care units due to its high mortality rate. Early identification and intervention are paramount to improving patient outcomes. In this study, we have proposed predictive models for early sepsis prediction based on time-series data, utilizing both CNN-Transformer and LSTM-Transformer architectures. By collecting time-series data from patients at 4, 8, and 12 h prior to sepsis diagnosis and subjecting it to various network models for analysis and comparison. In contrast to traditional recurrent neural networks, our model exhibited a substantial improvement of approximately 20%. On average, our model demonstrated an accuracy of 0.964 (± 0.018), a precision of 0.956 (± 0.012), a recall of 0.967 (± 0.012), and an F1 score of 0.959 (± 0.014). Furthermore, by adjusting the time window, it was observed that the Transformer-based model demonstrated exceptional predictive capabilities, particularly within the earlier time window (i.e., 12 h before onset), thus holding significant promise for early clinical diagnosis and intervention. Besides, we employed the SHAP algorithm to visualize the weight distribution of different features, enhancing the interpretability of our model and facilitating early clinical diagnosis and intervention.

show abstract

“…This signi cance arises from its dual role in not only facilitating the evaluation of disease severity but also in enhancing treatment strategies, mitigating unfavorable outcomes, and ultimately prolonging patients' lives. Currently, various clinical scoring systems, such as the Sequential Organ Failure Assessment (SOFA) score [5], the Acute Physiology and Chronic Health Evaluation (APACHE-II) score [6], and the PIRO score [7], can assist clinical practitioners in evaluating patients' overall risk and prognosis. Nonetheless, these scoring systems are designed for a broader range of critically ill patients and are not speci cally tailored to sepsis.…”

Section: Introductionmentioning

confidence: 99%

A Time Series Driven Model for Early Sepsis Prediction Based on Transformer Module

Tang,

Zhang,

2023

Preprint

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Sepsis remains a critical concern in intensive care units due to its high mortality rate. Early identification and intervention are paramount to improving patient outcomes. In this study, we have proposed predictive models for early sepsis prediction based on time-series data, utilizing both CNN-Transformer and LSTM-Transformer architectures. By collecting time-series data from patients at 4, 8, and 12 hours prior to sepsis diagnosis and subjecting it to various network models for analysis and comparison. In contrast to traditional recurrent neural networks, our model exhibited a substantial improvement of approximately 20%. On average, our model demonstrated an accuracy of 0.964 (± 0.018), a precision of 0.956 (± 0.012), a recall of 0.967 (± 0.012), and an F1 score of 0.959 (± 0.014). Furthermore, by adjusting the time window, it was observed that the Transformer-based model demonstrated exceptional predictive capabilities, particularly within the earlier time window (i.e., 12 hours before onset), thus holding significant promise for early clinical diagnosis and intervention. Besides, we employed the SHAP algorithm to visualize the weight distribution of different features, enhancing the interpretability of our model and facilitating early clinical diagnosis and intervention.

show abstract

Comparison of PIRO, APACHE IV, and SOFA Scores in Predicting Outcome in Patients with Sepsis Admitted to Intensive Care Unit: A Two-year Cross-sectional Study at Rural Teaching Hospital

Cited by 5 publications

References 15 publications

Utility of Platelet Indices as Prognostic Markers of Sepsis: A Medical Intensive Care Unit-Based Cross-Sectional Study at a Rural Setup

Utility of Platelet Indices as Prognostic Markers of Sepsis: A Medical Intensive Care Unit-Based Cross-Sectional Study at a Rural Setup

A time series driven model for early sepsis prediction based on transformer module

A Time Series Driven Model for Early Sepsis Prediction Based on Transformer Module

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