Development and Validation of a Policy Tree Approach for Optimizing Intravenous Fluids in Critically Ill Patients with Sepsis and Acute Kidney Injury.

Purpose: Intravenous fluids are mainstay of management of acute kidney injury (AKI) after sepsis but can cause fluid overload. Recent literature shows that restrictive fluid strategy may be beneficial in some patients with AKI, however, identifying these patients is challenging. We aimed to develop and validate a machine learning algorithm to identify patients who would benefit from a restrictive fluid strategy. Methods: We included patients with sepsis who developed AKI within 48 hours of ICU admission and defined restrictive fluid strategy as receiving <500mL fluids within 24 hours after AKI. Our primary outcome was early AKI reversal within 48 hours of AKI onset, and secondary outcomes included sustained AKI reversal and major adverse kidney events (MAKE) at discharge. We used a causal forest, a machine learning algorithm to estimate individual treatment effects and policy tree algorithm to identify patients who would benefit by restrictive fluid strategy. We developed the algorithm in MIMIC-IV and validated it in eICU database. Results: Among 2,091 patients in the external validation cohort, policy tree recommended restrictive fluids for 88.2%. Among these, patients who received restrictive fluids demonstrated significantly higher rate of early AKI reversal (48.2% vs 39.6%, p<0.001), sustained AKI reversal (36.7% vs 27.4%, p<0.001) and lower rates of MAKE by discharge (29.3% vs 35.1%, p=0.019). These results were consistent in adjusted analysis. Conclusion: Policy tree based on causal machine learning can identify septic patients with AKI who benefit from a restrictive fluid strategy. This approach needs to be validated in prospective trials.

Development and Validation of a Deep Learning Classifier Using Chest Radiographs to Predict Extubation Success in Patients Undergoing Invasive Mechanical Ventilation.

The decision to extubate patients on invasive mechanical ventilation is critical; however, clinician performance in identifying patients to liberate from the ventilator is poor. Machine Learning-based predictors using tabular data have been developed; however, these fail to capture the wide spectrum of data available. Here, we develop and validate a deep learning-based model using routinely collected chest X-rays to predict the outcome of attempted extubation. We included 2288 serial patients admitted to the Medical ICU at an urban academic medical center, who underwent invasive mechanical ventilation, with at least one intubated CXR, and a documented extubation attempt. The last CXR before extubation for each patient was taken and split 79/21 for training/testing sets, then transfer learning with k-fold cross-validation was used on a pre-trained ResNet50 deep learning architecture. The top three models were ensembled to form a final classifier. The Grad-CAM technique was used to visualize image regions driving predictions. The model achieved an AUC of 0.66, AUPRC of 0.94, sensitivity of 0.62, and specificity of 0.60. The model performance was improved compared to the Rapid Shallow Breathing Index (AUC 0.61) and the only identified previous study in this domain (AUC 0.55), but significant room for improvement and experimentation remains.

The COVID-19 Tracheostomy Experience at a Large Academic Medical Center in New York during the First Year.

Background: New York City was the epicenter of the initial surge of the COVID-19 pandemic in the United States. Tracheostomy is a critical procedure in the care of patients with COVID-19. We hypothesized that early tracheostomy would decrease the length of time on sedation, time on mechanical ventilation, intensive care unit length of stay, and mortality. Methods: A retrospective analysis of outcomes for all patients with COVID-19 who underwent tracheostomy during the first year of the COVID-19 pandemic at the Mount Sinai Hospital in New York City, New York. All adult intensive care units at the Mount Sinai Hospital, New York. Patients/subjects: 888 patients admitted to intensive care with COVID-19. Results: All patients admitted to the intensive care unit with COVID-19 (888) from 1 March 2020 to 1 March 2021 were analyzed and separated further into those intubated (544) and those requiring tracheostomy (177). Of those receiving tracheostomy, outcomes were analyzed for early (≤12 days) or late (>12 days) tracheostomy. Demographics, medical history, laboratory values, type of oxygen and ventilatory support, and clinical outcomes were recorded and analyzed. Conclusions: Early tracheostomy resulted in reduced duration of mechanical ventilation, reduced hospital length of stay, and reduced intensive care unit length of stay in patients admitted to the intensive care unit with COVID-19. There was no effect on overall mortality.

A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19.

Computational subphenotyping, a data-driven approach to understanding disease subtypes, is a prominent topic in medical research. Numerous ongoing studies are dedicated to developing advanced computational subphenotyping methods for cross-sectional data. However, the potential of time-series data has been underexplored until now. Here, we propose a Multivariate Levenshtein Distance (MLD) that can account for address correlation in multiple discrete features over time-series data. Our algorithm has two distinct components: it integrates an optimal threshold score to enhance the sensitivity in discriminating between pairs of instances, and the MLD itself. We have applied the proposed distance metrics on the k-means clustering algorithm to derive temporal subphenotypes from time-series data of biomarkers and treatment administrations from 1039 critically ill patients with COVID-19 and compare its effectiveness to standard methods. In conclusion, the Multivariate Levenshtein Distance metric is a novel method to quantify the distance from multiple discrete features over time-series data and demonstrates superior clustering performance among competing time-series distance metrics.

A Hybrid Decision Tree and Deep Learning Approach Combining Medical Imaging and Electronic Medical Records to Predict Intubation Among Hospitalized Patients With COVID-19: Algorithm Development and Validation.

BACKGROUND: Early prediction of the need for invasive mechanical ventilation (IMV) in patients hospitalized with COVID-19 symptoms can help in the allocation of resources appropriately and improve patient outcomes by appropriately monitoring and treating patients at the greatest risk of respiratory failure. To help with the complexity of deciding whether a patient needs IMV, machine learning algorithms may help bring more prognostic value in a timely and systematic manner. Chest radiographs (CXRs) and electronic medical records (EMRs), typically obtained early in patients admitted with COVID-19, are the keys to deciding whether they need IMV. OBJECTIVE: We aimed to evaluate the use of a machine learning model to predict the need for intubation within 24 hours by using a combination of CXR and EMR data in an end-to-end automated pipeline. We included historical data from 2481 hospitalizations at The Mount Sinai Hospital in New York City. METHODS: CXRs were first resized, rescaled, and normalized. Then lungs were segmented from the CXRs by using a U-Net algorithm. After splitting them into a training and a test set, the training set images were augmented. The augmented images were used to train an image classifier to predict the probability of intubation with a prediction window of 24 hours by retraining a pretrained DenseNet model by using transfer learning, 10-fold cross-validation, and grid search. Then, in the final fusion model, we trained a random forest algorithm via 10-fold cross-validation by combining the probability score from the image classifier with 41 longitudinal variables in the EMR. Variables in the EMR included clinical and laboratory data routinely collected in the inpatient setting. The final fusion model gave a prediction likelihood for the need of intubation within 24 hours as well. RESULTS: At a prediction probability threshold of 0.5, the fusion model provided 78.9% (95% CI 59%-96%) sensitivity, 83% (95% CI 76%-89%) specificity, 0.509 (95% CI 0.34-0.67) F1-score, 0.874 (95% CI 0.80-0.94) area under the receiver operating characteristic curve (AUROC), and 0.497 (95% CI 0.32-0.65) area under the precision recall curve (AUPRC) on the holdout set. Compared to the image classifier alone, which had an AUROC of 0.577 (95% CI 0.44-0.73) and an AUPRC of 0.206 (95% CI 0.08-0.38), the fusion model showed significant improvement (P<.001). The most important predictor variables were respiratory rate, C-reactive protein, oxygen saturation, and lactate dehydrogenase. The imaging probability score ranked 15th in overall feature importance. CONCLUSIONS: We show that, when linked with EMR data, an automated deep learning image classifier improved performance in identifying hospitalized patients with severe COVID-19 at risk for intubation. With additional prospective and external validation, such a model may assist risk assessment and optimize clinical decision-making in choosing the best care plan during the critical stages of COVID-19.

The fourth wave: vaccination status and intensive care unit mortality at a large hospital system in New York City.

BACKGROUND: We aim to describe the demographics and outcomes of patients with severe disease with the Omicron variant. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) virus continues to mutate, and the availability of vaccines and boosters continue to rise, it is important to understand the health care burden of new variants. We analyze patients admitted to intensive care units (ICUs) in a large Academic Health System during New York City's fourth surge beginning on November 27, 2021. METHODS: All patients admitted to an ICU were included in the primary analysis. Key demographics and outcomes were retrospectively compared between patients stratified by vaccination status. Univariate and multivariate logistic regression was used to identify risk factors for in-hospital mortality. RESULTS: In-hospital mortality for all admitted patients during the fourth wave was significantly lower than in previous waves. However, among patients requiring intensive care, in-hospital mortality was high across all levels of vaccination status. In a multivariate model older age was associated with increased in-hospital mortality, vaccination status of overdue for booster was associated with decreased in hospital mortality, and vaccination status of up-to-date with vaccination showed a trend to reduced mortality. CONCLUSIONS: In-hospital mortality of patients with severe respiratory failure from coronavirus disease 2019 (COVID-19) remains high despite decreasing overall mortality. Vaccination against SARS-CoV-2 was protective against mortality. Vaccination remains the best and safest way to protect against serious illness and death from COVID-19. It remains unclear that any other treatment will have success in changing the natural history of the disease.

The Third Wave: Comparing Seasonal Trends in COVID-19 Patient Data at a Large Hospital System in New York City.

The third wave of COVID-19 is unique in that vaccines have been widely available; however, the highly transmissible Delta variant has been the predominant strain. Temporal changes of hospitalized patient characteristics should continue to be analyzed as COVID-19 progresses. OBJECTIVES: Compare the demographics and outcomes of hospitalized patients during New York City's third wave of COVID-19 to the first two waves. DESIGN SETTING AND PARTICIPANTS: Retrospective cohort study across five hospitals within Mount Sinai Health System, a quaternary academic medical system in New York City. Participants were adult inpatients admitted with COVID-19 identified by positive severe acute respiratory syndrome coronavirus 2 polymerase chain reaction at admission or clinical documentation of infection during the three waves of COVID-19. MAIN OUTCOMES AND MEASURES: Patient demographics, comorbidities, vaccination status, and outcomes of COVID-19 patients hospitalized at Mount Sinai Health System were examined. Patients admitted during the third wave were notably younger than the first two, were mostly unvaccinated against COVID-19, and there was a higher rate of patients who self-report as Black or African American as compared with the first two waves. The rate of patients requiring ICU level of care remained consistent throughout all three periods; however, the rate of patients requiring invasive mechanical ventilation decreased and inhospital mortality has trended down. Unvaccinated patients in the third wave are significantly younger with lower comorbidity burden than fully vaccinated patients. RESULTS: A total of 13,036 patients were included between the 3 waves. In the 3rd wave patients were notably younger, with a lower intubation rate and lower inhospital death rate. In the 3rd wave, 400 (62.9%) were unvaccinated, 236 (37.1%) were fully vaccinated, and 34 (4.8%) were partially vaccinated. Unvaccinated patients had similar rates of intubation and invasive mechanical ventilation compared with vaccinated patients, though inhospital mortality was lower in unvaccinated patients compared with vaccinated patients which may be expected given their lower age and burden of comorbidities. CONCLUSIONS AND RELEVANCE: We continue to see improved outcomes in hospitalized COVID-19 patients. Patients that are unvaccinated against COVID-19 are younger and have less reported comorbidities.

Utility of electrophysiologic testing for sudden death risk stratification in cardiac sarcoidosis patients with mildly impaired left ventricular function.

BACKGROUND: Ventricular arrhythmias (VA) account for at least 25% of deaths caused by cardiac sarcoidosis (CS) and may arise in patients with mildly impaired LVEF (>35%). OBJECTIVE: In the current study, we examine whether EP study may be used for sudden death risk stratification in CS patients who have mildly impaired LVEF and a diagnosis of highly probable or probable CS according to the World Association of Sarcoidosis and Other Granulomatous Diseases Sarcoidosis Organ Assessment Instrument (WASOGI). METHODS: All patients: (1) exhibited a diagnosis of highly probable or probable CS according to the WASOGI, (2) exhibited cardiac MRI findings consistent with CS, (3) exhibited LVEF >45% and (4) underwent EP study. Device interrogations, transmissions and medical records were reviewed for all patients. RESULTS: We identified 46 CS patients with mildly impaired LVEF. VA were induced in 11 patients and 10/11 patients underwent ICD placement. Thirty-five patients had no VA and 24/35 patients underwent placement of an ILR. During the follow-up period, the VA event rate was 6.5%. The negative and positive predictive values of the EP study for the development of VA were 100% and 27.2%, respectively. CONCLUSIONS: In CS patients with mildly impaired LVEF and a diagnosis of highly probable or probable CS, a negative EP study was highly predictive of the absence of VA. The successful execution of future prospective studies is contingent upon enrollment of phenotypically homogenous cardiac sarcoidosis patients.

The Mount Sinai Hospital Institute for critical care medicine response to the COVID-19 pandemic.

BACKGROUND: The coronavirus disease 2019 (COVID-19) pandemic resulted in a surge of critically ill patients. This was especially true in New York City. We present a roadmap for hospitals and healthcare systems to prepare for a Pandemic. METHODS: This was a retrospective review of how Mount Sinai Hospital (MSH) was able to rapidly prepare to handle the pandemic. MSH, the largest academic hospital within the Mount Sinai Health System, rapidly expanded the intensive care unit (ICU) bed capacity, including creating new ICU beds, expanded the workforce, and created guidelines. RESULTS: MSH a 1,139-bed quaternary care academic referral hospital with 104 ICU beds expanded to 1,453 beds (27.5% increase) with 235 ICU beds (126% increase) during the pandemic peak in the first week of April 2020. From March to June 2020, with follow-up through October 2020, MSH admitted 2,591 COVID-19-positive patients, 614 to ICUs. Most admitted patients received noninvasive support including a non-rebreather mask, high flow nasal cannula, and noninvasive positive pressure ventilation. Among ICU patients, 68.4% (n=420) received mechanical ventilation; among the admitted ICU patients, 42.8% (n=263) died, and 47.8% (n=294) were discharged alive. CONCLUSIONS: Flexible bed management initiatives; teamwork across multiple disciplines; and development and implementation of guidelines were critical accommodating the surge of critically ill patients. Non-ICU services and staff were deployed to augment the critical care work force and open new critical care units. This approach to rapidly expand bed availability and staffing across the system helped provide the best care for the patients and saved lives.

Comparing Seasonal Trends in Coronavirus Disease 2019 Patient Data at a Quaternary Hospital in New York City.

Coronavirus disease 2019 has been a worldwide pandemic since early 2020 with New York City being the epicenter in the United States during early 2020. Although cases of decreased coronavirus disease 2019 during the summer, cases began to rise once more in the fall-winter period. Little is known about trends in patient characteristics, medical care, and outcome between these time periods. We report initial patient characteristics and outcomes from a large quaternary referral center in New York City between Spring (March to June), Summer (July to September), and Winter (October to December), including prevalence of renal failure, respiratory failure, and mortality; stratified across several key populations of interest including all patients, ICU patients, those requiring of noninvasive positive pressure ventilation and high-flow nasal cannula, and those intubated in each time period.

Cardiovascular Disease and Severe Hypoxemia Are Associated With Higher Rates of Noninvasive Respiratory Support Failure in Coronavirus Disease 2019 Pneumonia.

Acute hypoxemic respiratory failure is the major complication of coronavirus disease 2019, yet optimal respiratory support strategies are uncertain. We aimed to describe outcomes with high-flow oxygen delivered through nasal cannula and noninvasive positive pressure ventilation in coronavirus disease 2019 acute hypoxemic respiratory failure and identify individual factors associated with noninvasive respiratory support failure. DESIGN: Retrospective cohort study to describe rates of high-flow oxygen delivered through nasal cannula and/or noninvasive positive pressure ventilation success (live discharge without endotracheal intubation). Fine-Gray subdistribution hazard models were used to identify patient characteristics associated with high-flow oxygen delivered through nasal cannula and/or noninvasive positive pressure ventilation failure (endotracheal intubation and/or in-hospital mortality). SETTING: One large academic health system, including five hospitals (one quaternary referral center, a tertiary hospital, and three community hospitals), in New York City. PATIENTS: All hospitalized adults 18-100 years old with coronavirus disease 2019 admitted between March 1, 2020, and April 28, 2020. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: A total of 331 and 747 patients received high-flow oxygen delivered through nasal cannula and noninvasive positive pressure ventilation as the highest level of noninvasive respiratory support, respectively; 154 (46.5%) in the high-flow oxygen delivered through nasal cannula cohort and 167 (22.4%) in the noninvasive positive pressure ventilation cohort were successfully discharged without requiring endotracheal intubation. In adjusted models, significantly increased risk of high-flow oxygen delivered through nasal cannula and noninvasive positive pressure ventilation failure was seen among patients with cardiovascular disease (subdistribution hazard ratio, 1.82; 95% CI, 1.17-2.83 and subdistribution hazard ratio, 1.40; 95% CI, 1.06-1.84, respectively). Conversely, a higher peripheral blood oxygen saturation to Fio2 ratio at high-flow oxygen delivered through nasal cannula and noninvasive positive pressure ventilation initiation was associated with reduced risk of failure (subdistribution hazard ratio, 0.32; 95% CI, 0.19-0.54, and subdistribution hazard ratio 0.34; 95% CI, 0.21-0.55, respectively). CONCLUSIONS: A significant proportion of patients receiving noninvasive respiratory modalities for coronavirus disease 2019 acute hypoxemic respiratory failure achieved successful hospital discharge without requiring endotracheal intubation, with lower success rates among those with comorbid cardiovascular disease or more severe hypoxemia. The role of high-flow oxygen delivered through nasal cannula and noninvasive positive pressure ventilation in coronavirus disease 2019-related acute hypoxemic respiratory failure warrants further consideration.

Timing of Intubation and In-Hospital Mortality in Patients With Coronavirus Disease 2019.

OBJECTIVES: To examine whether increasing time between admission and intubation was associated with mortality in patients with coronavirus disease 2019 who underwent mechanical ventilation. DESIGN: Retrospective cohort study of patients with severe acute respiratory syndrome coronavirus 2 infection who were admitted between January 30, 2020, and April 30, 2020, and underwent intubation and mechanical ventilation prior to May 1, 2020. Patients were followed up through August 15, 2020. SETTING: Five hospitals within the Mount Sinai Health System in New York City, NY. PATIENTS: Adult patients with severe acute respiratory syndrome coronavirus 2 infection who underwent intubation and mechanical ventilation. INTERVENTIONS: Tracheal intubation and mechanical ventilation. MEASUREMENTS AND MAIN RESULTS: The primary outcome was in-hospital mortality. A hospital-stratified time-varying Cox model was used to evaluate the effect of time from admission to intubation on in-hospital death. A total of 755 adult patients out of 5,843 admitted with confirmed severe acute respiratory syndrome coronavirus 2 infection underwent tracheal intubation and mechanical ventilation during the study period. The median age of patients was 65 years (interquartile range, 56-72 yr) and 64% were male. As of the time of follow-up, 121 patients (16%) who were intubated and mechanically ventilated had been discharged home, 512 (68%) had died, 113 (15%) had been discharged to a skilled nursing facility, and 9 (1%) remained in the hospital. The median time from admission to intubation was 2.3 days (interquartile range, 0.6-6.3 d). Each additional day between hospital admission and intubation was significantly associated with higher in-hospital death (adjusted hazard ratio, 1.03; 95% CI, 1.01-1.05). CONCLUSIONS: Among patients with coronavirus disease 2019 who were intubated and mechanically ventilated, intubation earlier in the course of hospital admission may be associated with improved survival.

Convalescent plasma treatment of severe COVID-19: a propensity score-matched control study.

Coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), is a new human disease with few effective treatments1. Convalescent plasma, donated by persons who have recovered from COVID-19, is the acellular component of blood that contains antibodies, including those that specifically recognize SARS-CoV-2. These antibodies, when transfused into patients infected with SARS-CoV-2, are thought to exert an antiviral effect, suppressing virus replication before patients have mounted their own humoral immune responses2,3. Virus-specific antibodies from recovered persons are often the first available therapy for an emerging infectious disease, a stopgap treatment while new antivirals and vaccines are being developed1,2. This retrospective, propensity score-matched case-control study assessed the effectiveness of convalescent plasma therapy in 39 patients with severe or life-threatening COVID-19 at The Mount Sinai Hospital in New York City. Oxygen requirements on day 14 after transfusion worsened in 17.9% of plasma recipients versus 28.2% of propensity score-matched controls who were hospitalized with COVID-19 (adjusted odds ratio (OR), 0.86; 95% confidence interval (CI), 0.75-0.98; chi-square test P value = 0.025). Survival also improved in plasma recipients (adjusted hazard ratio (HR), 0.34; 95% CI, 0.13-0.89; chi-square test P = 0.027). Convalescent plasma is potentially effective against COVID-19, but adequately powered, randomized controlled trials are needed.

Using Machine Learning to Predict ICU Transfer in Hospitalized COVID-19 Patients.

OBJECTIVES: Approximately 20-30% of patients with COVID-19 require hospitalization, and 5-12% may require critical care in an intensive care unit (ICU). A rapid surge in cases of severe COVID-19 will lead to a corresponding surge in demand for ICU care. Because of constraints on resources, frontline healthcare workers may be unable to provide the frequent monitoring and assessment required for all patients at high risk of clinical deterioration. We developed a machine learning-based risk prioritization tool that predicts ICU transfer within 24 h, seeking to facilitate efficient use of care providers' efforts and help hospitals plan their flow of operations. METHODS: A retrospective cohort was comprised of non-ICU COVID-19 admissions at a large acute care health system between 26 February and 18 April 2020. Time series data, including vital signs, nursing assessments, laboratory data, and electrocardiograms, were used as input variables for training a random forest (RF) model. The cohort was randomly split (70:30) into training and test sets. The RF model was trained using 10-fold cross-validation on the training set, and its predictive performance on the test set was then evaluated. RESULTS: The cohort consisted of 1987 unique patients diagnosed with COVID-19 and admitted to non-ICU units of the hospital. The median time to ICU transfer was 2.45 days from the time of admission. Compared to actual admissions, the tool had 72.8% (95% CI: 63.2-81.1%) sensitivity, 76.3% (95% CI: 74.7-77.9%) specificity, 76.2% (95% CI: 74.6-77.7%) accuracy, and 79.9% (95% CI: 75.2-84.6%) area under the receiver operating characteristics curve. CONCLUSIONS: A ML-based prediction model can be used as a screening tool to identify patients at risk of imminent ICU transfer within 24 h. This tool could improve the management of hospital resources and patient-throughput planning, thus delivering more effective care to patients hospitalized with COVID-19.

Nef interaction with actin compromises human podocyte actin cytoskeletal integrity.

The HIV-1 accessory protein Nef is considered to play an important role in the development of a podocyte phenotype in HIV-1 associated nephropathy. We hypothesized that Nef may be altering the podocyte phenotype both structurally and functionally. To elucidate the involved mechanisms, podocyte proteins interacting with Nef were identified using GST pull down assay and yeast two hybrid assay. The GST pull down assay on protein extracts made from stable colonies of conditionally immortalized human podocytes expressing Nef (Nef/CIHP) displayed a band at 45 kD, which was identified as actin by mass spectrometry. Yeast two hybrid assay identified the following Nef-interacting proteins: syntrophin, filamin B, syntaxin, translational elongation factor 1, and zyxin. The Nef-actin and Nef-zyxin interactions were confirmed by co-localization studies on Nef/CIHP stable cell lines. The co-localization studies also showed that Nef/CIHP stable cell lines had a decreased number of actin filaments (stress fibers), displayed formation of lamellipodia, and increased number of podocyte projections (filopodia). Nef/CIHP displayed an enhanced cortical F-actin score index (P<0.001) and thus indicated a reorganization of F-actin in the cortical regions. Microarray analysis showed that Nef enhanced the expression of Rac1, syndecan-4, Rif, and CDC42 and attenuated the expression of syndecan-3 and syntenin. In addition, Nef/CIHPs displayed a diminished sphingomyelinase (ASMase) activity. Functionally, Nef/CIHPs displayed diminished attachment and enhanced detachment to their substrate. These findings indicate that Nef interaction with actin compromises the podocyte cytoskeleton integrity.

HIV gene expression deactivates redox-sensitive stress response program in mouse tubular cells both in vitro and in vivo.

Human immunodeficiency virus (HIV)-1 has been reported to cause tubular cell injury both in in vivo and in vitro studies. In the present study, we evaluated the role of oxidative stress in the induction of apoptosis in HIV gene expressing mouse tubular cells in in vivo (Tg26, a transgenic mouse model of HIV-associated nephropathy) and in vitro (tubular cells were transduced with pNL4-3: ΔG/P-GFP, VSV.G psueudo typed virus) studies. Although Tg26 mice showed enhanced tubular cell reactive oxygen species (ROS) generation and apoptosis, renal tissue did not display a robust antioxidant response in the form of enhanced free radical scavenger (MnSOD/catalase) expression. Tg26 mice not only showed enhanced tubular cell expression of phospho-p66ShcA but also displayed nuclear Foxo3a translocation to the cytoplasm. These findings indicated deactivation of tubular cell Foxo3A-dependent redox-sensitive stress response program (RSSRP) in Tg26 mice. In in vitro studies, NL4-3 (pNL4-3: ΔG/P-GFP, VSV.G pseudotyped virus)-transduced mouse proximal tubular cells (NL4-3/MPTEC) displayed enhanced phosphorylation of p66ShcA. NL4-3/MPTECs also displayed greater (P < 0.01) ROS generation when compared with empty vector-transduced tubular cells; however, both diminution of p66ShcA and N-acetyl cysteine attenuated NL4-3-induced tubular cell ROS generation as well as apoptosis. In addition, both antioxidants and free radical scavengers partially inhibited HIV-induced tubular cell apoptosis. NL4-3/MPTEC displayed deactivation of RSSRP in the form of enhanced phosphorylation of Foxo3A and attenuated expression of superoxide dismutase (SOD) and catalase. Since both SOD and catalase were able to provide protection against HIV-1-induced tubular cell apoptosis, it suggests that HIV-1-induced proapoptotic effect may be a consequence of the deactivated RSSRP.