Accuracy of Estimated Continuous Cardiac Output Monitoring (esCCO) Using Pulse Wave Transit Time (PWTT) Compared to Arterial Pressure-based CO (APCO) Measurement during Major Surgeries.

Background: Pulse wave transit time is a novel method of estimating continuous cardiac output (esCCO). Since there are not many studies evaluating esCCO, we compared it with arterial pressure based cardiac output (APCO) method (FloTrac). Methods: In this prospective single-center observational study, we included 50 adult patients planned to undergo supramajor oncosurgeries, where major blood loss and extensive fluid shifts were expected. Cardiac output (CO) measurements were obtained by both methods at five distinct time points, giving us 250 paired readings of stroke volume index (SVI) and cardiac index (CI). We analyzed these readings using Pearson's correlation coefficient and Bland-Altman plots, along with other appropriate statistical tests. Results: There was significant correlation between CI and SVI measured by the esCCO and APCO. Bland-Altman plot analysis for CI showed a bias of -0.44 L/minute/m2, precision of 0.74, and the limits of agreement of -1.89 and +1.01, while the percentage error was 46.29%. Bland-Altman analysis for SVI showed a bias -5.07 mL with a precision of 9.36, and the limits of agreement to be -23.4 to +13.28. The percentage error was 46.56%. Conclusion: This study demonstrated that esCCO tended to underestimate the CI to a large degree, particularly while estimating the cardiac output in the lower range. We found that the limits of agreement between two methods were wide, which are not likely to be clinically acceptable. Further studies with larger number of data points, obtained in a similar subset of patients, for cardiac output measurement in the perioperative period will certainly help determine if pulse wave transit time (PWTT) is here to stay (CTRI No.: CTRI/2019/08/020543). How to cite this article: Joshi M, Rathod R, Bhosale SJ, Kulkarni AP. Accuracy of Estimated Continuous Cardiac Output Monitoring (esCCO) Using Pulse Wave Transit Time (PWTT) Compared to Arterial Pressure-based CO (APCO) Measurement during Major Surgeries. Indian J Crit Care Med 2022;26(4):496-500.

Correlation between Carotid and Brachial Artery Velocity Time Integral and Their Comparison to Pulse Pressure Variation and Stroke Volume Variation for Assessing Fluid Responsiveness.

Background: Fluid boluses are used in hemodynamically unstable patients with presumed hypovolemia, to improve tissue perfusion, in the perioperative period. Now less invasive methods, such as pulse pressure variation (PPV) and stroke volume variation (SVV) are increasingly being used. We investigated correlation between carotid and brachial artery velocity time integral (VTI) and compared both with PPV and SVV. Methods: We recruited 27 patients undergoing supra-major abdominal surgeries. When indicated (hypotension or increased lactate), a fluid bolus was given after measuring carotid and brachial artery VTI, PPV, and SVV. The change in SV was noted and patients were categorized as responders if the SV increased by >15%. We performed Bland Altman Agreement and calculated best sensitivity and specificity for the parameters. Results: Patients were found to be fluid responders on 29 instances. The correlation between PPV, SVV, carotid and brachial artery VTI was poor and the limits of agreement between them were wide. The Area under Curve (AUC) for PPV was 0.69, for SVV was 0.63, while those of Carotid and Brachial artery VTI (TAP and flow) were (0.53 and 0.54 for carotid) and (0.51 and 0.56 for brachial) respectively. Conclusion: We found poor agreement and weak correlation between both VTi (TAP and flow) measured at carotid and brachial arteries, suggesting that the readings at brachial vessel cannot be used interchangeably with those at carotid artery. The PPV and SVV were better than these parameters for predicting fluid responsiveness; however, their predictive ability (AUROC), sensitivity and specificity were much lower than previously reported. Further studies in this area are therefore required (CTRI Reg No: CTRI/2017/08/009243). How to cite this article: Joshi M, Dhakane P, Bhosale SJ, Phulambrikar R, Kulkarni AP. Correlation between Carotid and Brachial Artery Velocity Time Integral and Their Comparison to Pulse Pressure Variation and Stroke Volume Variation for Assessing Fluid Responsiveness. Indian J Crit Care Med 2022;26(2):179-184.

Epidemiology and Predictors of Hospital Outcomes of Critically Ill Pediatric Oncology Patients: A Retrospective Study.

BACKGROUND: The number of pediatric oncology patients admitted to the intensive care unit (ICU) has increased, and their hospital outcomes are improving. Since scarce data are available about this patient population, we conducted this retrospective study to evaluate the epidemiology and predictors of hospital outcomes. MATERIALS AND METHODS: We included all children with cancers who were admitted to our ICU over 1 year. We excluded children admitted after elective surgery and those following bone marrow transplant. We collected data about demographics, admission diagnosis, type of malignancies, and ICU interventions. The primary outcome was the hospital outcome. The secondary outcomes were ICU length of stay (LOS), and ICU and hospital mortality. We analyzed the predictors of hospital outcome. RESULTS: Two hundred pediatric oncology patients were admitted from November 1, 2014 to October 30, 2015. Seventy-eight children had solid organ malignancies, and the rest had hematological malignancies. Hematooncology malignancy patients had significantly higher hospital mortality than those with solid organ malignancies. (61.5 vs 34.6%, p = 0.015). On multivariate regression analysis, mechanical ventilation [odds ratio (OR), 14.64; 95% confidence interval (CI): 1.23-165.05; p <0.030], inotropes (OR, 9.81; 95% CI: 1.222-78.66; p <0.032), and the presence of coagulopathy (OR, 3.86; 95% CI: 1.568-9.514; p <0.003) were independent predictors of hospital mortality. CONCLUSION: In this retrospective cohort of 200 children with malignancies, we found that children with hematologic cancer had significantly higher hospital mortality as compared to those with solid tumors. The need for mechanical ventilation, use of inotrope infusion, and coagulopathy were independent predictors of mortality. HOW TO CITE THIS ARTICLE: Bhosale SJ, Joshi M, Patil VP, Kothekar AT, Myatra SN, Divatia JV, et al. Epidemiology and Predictors of Hospital Outcomes of Critically Ill Pediatric Oncology Patients: A Retrospective Study. Indian J Crit Care Med 2021;25(10):1183-1188.

Increasing the Safety of Percutaneous Dilatational Tracheostomy in COVID-19 Patients.

How to cite this article: Bhosale SJ, Khatib KI. Increasing the Safety of Percutaneous Dilatational Tracheostomy in COVID-19 Patients. Indian J Crit Care Med 2021;25(6):610-612.

Incidence and Predictors of Acute Kidney Injury in Patients Undergoing Elective Hepatic Resection for Malignant Tumors: A 3-year Prospective Observational Study.

BACKGROUND: Acute kidney injury (AKI) is common in patients undergoing major surgeries, and leads to the need for renal replacement therapy and increased morbidity, intensive care unit (ICU) and hospital length of stay (LOS), cost, and mortality. We evaluated the incidence and predictors of postoperative AKI in patients undergoing hepatic resections and their short-term outcomes. MATERIALS AND METHODS: This prospective observational study was conducted over a 3-year period in 180 patients undergoing elective hepatic resections for a variety of indications. We used the Acute Kidney Injury Network criteria to determine the incidence of AKI at 72 hours. Perioperative variables contributing to the development of AKI and the short-term postoperative outcomes of patients were evaluated. RESULTS: Postoperative AKI occurred in 29.4% of patients. Persistent renal dysfunction was seen in five patients. Development of AKI was associated with hepatic failure (18.5 vs 5.5%, p < 0.005), prolonged ICU (2 vs 1 days, p < 0.001) and hospital LOS (11 vs 8 days, p < 0.004), and increased ICU and hospital mortality (9.6 vs 1.4%, p < 0.02). Age [OR (odds ratio) 1.033, 95% CI (confidence interval) 1.003-1.065, p = 0.03], BMI (body mass index) (OR 1.131, 95% CI 1.043-1.227, p = 0.003), and need for postoperative ventilation (OR 3.456, 95% CI 1.593-7.495, p = 0.002) were independent predictors of AKI. CONCLUSION: AKI after elective hepatic resection occurred in nearly one-third of our patients. Persistent renal dysfunction was seen in five patients. Age, BMI, and need for postoperative ventilation were independent predictors of postoperative AKI. (CTRI reg. No.: CTRI/2016/06/007044). HOW TO CITE THIS ARTICLE: Joshi M, Milmile R, Dhakane P, Bhosale SJ, Kulkarni AP. Incidence and Predictors of Acute Kidney Injury in Patients Undergoing Elective Hepatic Resection for Malignant Tumors: A 3-year Prospective Observational Study. Indian J Crit Care Med 2021;25(4):398-404.

Epidemiology and Pathogenesis of Acute Kidney Injury in the Critically Ill Patients.

How to cite this article: Kulkarni AP, Bhosale SJ. Epidemiology and Pathogenesis of Acute Kidney Injury in the Critically Ill Patients. Indian J Crit Care Med 2020;24(Suppl 3):S84-S89.

Biomarkers in Acute Kidney Injury.

How to cite this article: Bhosale SJ, Kulkarni AP. Biomarkers in Acute Kidney Injury. Indian J Crit Care Med 2020;24(Suppl 3):S90-S93.

Preventing Perioperative Acute Kidney Injury.

How to cite this article: Bhosale SJ, Kulkarni AP. Preventing Perioperative Acute Kidney Injury. Indian J Crit Care Med 2020;24(Suppl 3):S126-S128.

Crystal Gazing: Myth or Reality for Critical Care for COVID-19 Patients?

Efforts are continuing worldwide to understand the epidemiology, pathogenesis, and treatments for coronavirus disease-2019 (COVID-19). However, at the moment treatment remains supportive with oxygen therapy, steroids, repurposed antivirals, and prevention of multiple organ dysfunction by using immunomodulators. COVID-19 remains challenging since the disease spectrum varies from asymptomatic infection to severe acute respiratory distress syndrome (ARDS) with high fatality rates. It is thus necessary to predict clinical outcomes and risk-stratify patients for ensuring early intensive care unit (ICU) admissions. An important aspect is building surge capacity, managing and optimizing therapeutic and operational resources. So far, data have been scarce, particularly from India, to identify predictors of poor outcomes and mortality early in the course of the disease. Risk models need to be developed in larger patient cohorts and the models need to be simple and easy to employ at the onset of the disease process to predict the risk of severe disease, need for mechanical ventilation, ICU length of stay (LOS), and mortality. How to cite this article: Bhosale SJ, Kulkarni AP. Crystal Gazing: Myth or Reality for Critical Care for COVID-19 Patients? Indian J Crit Care Med 2020;24(12):1161-1162.