- Interventional Pulmonology
- Thoracic Oncology
- Pulmonary Disease
- Pleural Diseases
- Pulmonary Nodules
Clinical Assistant Professor, Medicine - Pulmonary & Critical Care Medicine
Interventional Pulmonology Program Director, Stanford Cancer Center South Bay, Stanford Healthcare (2015 - Present)
Board Certification: Critical Care Medicine, American Board of Internal Medicine (2015)
Board Certification: Pulmonary Disease, American Board of Internal Medicine (2014)
Fellowship:Stanford University (2015) CA
Residency:Stanford University Medical Center (2012) CA
Medical Education:Stanford University (2008) CA
Board Certification: Internal Medicine, American Board of Internal Medicine (2011)
The Association of ICU Acuity With Outcomes of Patients at Low Risk of Dying.
Critical care medicine
2018; 46 (3): 347–53
Many ICU patients do not require critical care interventions. Whether aggressive care environments increase risks to low-acuity patients is unknown. We evaluated whether ICU acuity was associated with outcomes of low mortality-risk patients. We hypothesized that admission to high-acuity ICUs would be associated with worse outcomes. This hypothesis was based on two possibilities: 1) high-acuity ICUs may have a culture of aggressive therapy that could lead to potentially avoidable complications and 2) high-acuity ICUs may focus attention toward the many sicker patients and away from the fewer low-risk patients.Retrospective cohort study.Three hundred twenty-two ICUs in 199 hospitals in the Philips eICU database between 2010 and 2015.Adult ICU patients at low risk of dying, defined as an Acute Physiology and Chronic Health Evaluation-IVa-predicted mortality of 3% or less.ICU acuity, defined as the mean Acute Physiology and Chronic Health Evaluation IVa score of all admitted patients in a calendar year, stratified into quartiles.We used generalized estimating equations to test whether ICU acuity is independently associated with a primary outcome of ICU length of stay and secondary outcomes of hospital length of stay, hospital mortality, and discharge destination. The study included 381,997 low-risk patients. Mean ICU and hospital length of stay were 1.8 ± 2.1 and 5.2 ± 5.0 days, respectively. Mean Acute Physiology and Chronic Health Evaluation IVa-predicted hospital mortality was 1.6% ± 0.8%; actual hospital mortality was 0.7%. In adjusted analyses, admission to low-acuity ICUs was associated with worse outcomes compared with higher-acuity ICUs. Specifically, compared with the highest-acuity quartile, ICU length of stay in low-acuity ICUs was increased by 0.24 days; in medium-acuity ICUs by 0.16 days; and in high-acuity ICUs by 0.09 days (all p < 0.001). Similar patterns existed for hospital length of stay. Patients in lower-acuity ICUs had significantly higher hospital mortality (odds ratio, 1.28 [95% CI, 1.10-1.49] for low-; 1.24 [95% CI, 1.07-1.42] for medium-, and 1.14 [95% CI, 0.99-1.31] for high-acuity ICUs) and lower likelihood of discharge home (odds ratio, 0.86 [95% CI, 0.82-0.90] for low-, 0.88 [95% CI, 0.85-0.92] for medium-, and 0.95 [95% CI, 0.92-0.99] for high-acuity ICUs).Admission to high-acuity ICUs is associated with better outcomes among low mortality-risk patients. Future research should aim to understand factors that confer benefit to patients with different risk profiles.
View details for DOI 10.1097/CCM.0000000000002798
View details for PubMedID 29474319
View details for PubMedCentralID PMC5828025
Identifying Distinct Subgroups of ICU Patients: A Machine Learning Approach.
Critical care medicine
2017; 45 (10): 1607–15
Identifying subgroups of ICU patients with similar clinical needs and trajectories may provide a framework for more efficient ICU care through the design of care platforms tailored around patients' shared needs. However, objective methods for identifying these ICU patient subgroups are lacking. We used a machine learning approach to empirically identify ICU patient subgroups through clustering analysis and evaluate whether these groups might represent appropriate targets for care redesign efforts.We performed clustering analysis using data from patients' hospital stays to retrospectively identify patient subgroups from a large, heterogeneous ICU population.Kaiser Permanente Northern California, a healthcare delivery system serving 3.9 million members.ICU patients 18 years old or older with an ICU admission between January 1, 2012, and December 31, 2012, at one of 21 Kaiser Permanente Northern California hospitals.None.We used clustering analysis to identify putative clusters among 5,000 patients randomly selected from 24,884 ICU patients. To assess cluster validity, we evaluated the distribution and frequency of patient characteristics and the need for invasive therapies. We then applied a classifier built from the sample cohort to the remaining 19,884 patients to compare the derivation and validation clusters. Clustering analysis successfully identified six clinically recognizable subgroups that differed significantly in all baseline characteristics and clinical trajectories, despite sharing common diagnoses. In the validation cohort, the proportion of patients assigned to each cluster was similar and demonstrated significant differences across clusters for all variables.A machine learning approach revealed important differences between empirically derived subgroups of ICU patients that are not typically revealed by admitting diagnosis or severity of illness alone. Similar data-driven approaches may provide a framework for future organizational innovations in ICU care tailored around patients' shared needs.
View details for DOI 10.1097/CCM.0000000000002548
View details for PubMedID 28640021
View details for PubMedCentralID PMC5600667
Detecting organisational innovations leading to improved ICU outcomes: a protocol for a double-blinded national positive deviance study of critical care delivery.
2017; 7 (6): e015930
There is substantial variability in intensive care unit (ICU) utilisation and quality of care. However, the factors that drive this variation are poorly understood. This study uses a novel adaptation of positive deviance approach-a methodology used in public health that assumes solutions to challenges already exist within the system to detect innovations that are likely to improve intensive care.We used the Philips eICU Research Institute database, containing 3.3 million patient records from over 50 health systems across the USA. Acute Physiology and Chronic Health Evaluation IVa scores were used to identify the study cohort, which included ICU patients whose outcomes were felt to be most sensitive to organisational innovations. The primary outcomes included mortality and length of stay. Outcome measurements were directly standardised, and bootstrapped CIs were calculated with adjustment for false discovery rate. Using purposive sampling, we then generated a blinded list of five positive outliers and five negative comparators.Using rapid qualitative inquiry (RQI), blinded interdisciplinary site visit teams will conduct interviews and observations using a team ethnography approach. After data collection is completed, the data will be unblinded and analysed using a cross-case method to identify themes, patterns and innovations using a constant comparative grounded theory approach. This process detects the innovations in intensive care and supports an evaluation of how positive deviance and RQI methods can be adapted to healthcare.The study protocol was approved by the Stanford University Institutional Review Board (reference: 39509). We plan on publishing study findings and methodological guidance in peer-reviewed academic journals, white papers and presentations at conferences.
View details for DOI 10.1136/bmjopen-2017-015930
View details for PubMedID 28615274
View details for PubMedCentralID PMC5541524
Inadequacies of Physical Examination as a Cause of Medical Errors and Adverse Events: A Collection of Vignettes.
American journal of medicine
2015; 128 (12): 1322-1324 e3
Oversights in the physical examination are a type of medical error not easily studied by chart review. They may be a major contributor to missed or delayed diagnosis, unnecessary exposure to contrast and radiation, incorrect treatment, and other adverse consequences. Our purpose was to collect vignettes of physical examination oversights and to capture the diversity of their characteristics and consequences.A cross-sectional study using an 11-question qualitative survey for physicians was distributed electronically, with data collected from February to June of 2011. The participants were all physicians responding to e-mail or social media invitations to complete the survey. There were no limitations on geography, specialty, or practice setting.Of the 208 reported vignettes that met inclusion criteria, the oversight was caused by a failure to perform the physical examination in 63%; 14% reported that the correct physical examination sign was elicited but misinterpreted, whereas 11% reported that the relevant sign was missed or not sought. Consequence of the physical examination inadequacy included missed or delayed diagnosis in 76% of cases, incorrect diagnosis in 27%, unnecessary treatment in 18%, no or delayed treatment in 42%, unnecessary diagnostic cost in 25%, unnecessary exposure to radiation or contrast in 17%, and complications caused by treatments in 4%. The mode of the number of physicians missing the finding was 2, but many oversights were missed by many physicians. Most oversights took up to 5 days to identify, but 66 took longer. Special attention and skill in examining the skin and its appendages, as well as the abdomen, groin, and genitourinary area could reduce the reported oversights by half.Physical examination inadequacies are a preventable source of medical error, and adverse events are caused mostly by failure to perform the relevant examination.
View details for DOI 10.1016/j.amjmed.2015.06.004
View details for PubMedID 26144103
A Phase II Study of Gefitinib, 5-Fluorouracil, Leucovorin, and Oxaliplatin in Previously Untreated Patients with Metastatic Colorectal Cancer
CLINICAL CANCER RESEARCH
2008; 14 (21): 7074-7079
We investigated the gefitinib, 5-fluorouracil (5-FU), leucovorin and oxaliplatin (IFOX) regimen as first-line therapy in patients with metastatic colorectal cancer.Eligible patients had stage IV colorectal adenocarcinoma, and had not received prior chemotherapy for metastatic disease. Each cycle consisted of 14 days. Cycle 1 consisted of oxaliplatin, leucovorin, and 5-FU (FOLFOX-4). All subsequent cycles consisted of FOLFOX-4 with gefitinib at 500 mg orally daily throughout the 14-day cycle.Forty-five patients were enrolled and were assessable for toxicity. Forty-three patients were assessable for response. Thirty-one of the 43 patients (72%) had either a complete or partial response by the Response Evaluation Criteria in Solid Tumors. Median overall survival was 20.5 months. Median time to progression was 9.3 months. Commonly encountered grade 3 or 4 toxicities included diarrhea in 67% of patients and neutropenia in 60%. Grade 2 acneiform skin rash typical of gefitinib occurred in 60% of patients.IFOX is an active first-line regimen in patients with metastatic colorectal adenocarcinoma, showing higher response rates but also increased toxicities compared with FOLFOX-4 alone in a similar patient population.
View details for DOI 10.1158/1078-0432.CCR-08-1014
View details for Web of Science ID 000260732200044
View details for PubMedID 18981005
View details for PubMedCentralID PMC2583341
Differential gene expression patterns and interaction networks in BCR-ABL-positive and -negative adult acute lymphoblastic leukemias
JOURNAL OF CLINICAL ONCOLOGY
2007; 25 (11): 1341-1349
To identify gene expression patterns and interaction networks related to BCR-ABL status and clinical outcome in adults with acute lymphoblastic leukemia (ALL).DNA microarrays were used to profile a set of 54 adult ALL specimens from the Medical Research Council UKALL XII/Eastern Cooperative Oncology Group E2993 trial (21 p185BCR-ABL-positive, 16 p210BCR-ABL-positive and 17 BCR-ABL-negative specimens).Using supervised and unsupervised analysis tools, we detected significant transcriptomic changes in BCR-ABL-positive versus -negative specimens, and assessed their validity in an independent cohort of 128 adult ALL specimens. This set of 271 differentially expressed genes (including GAB1, CIITA, XBP1, CD83, SERPINB9, PTP4A3, NOV, LOX, CTNND1, BAALC, and RAB21) is enriched for genes involved in cell death, cellular growth and proliferation, and hematologic system development and function. Network analysis demonstrated complex interaction patterns of these genes, and identified FYN and IL15 as the hubs of the top-scoring network. Within the BCR-ABL-positive subgroups, we identified genes overexpressed (PILRB, STS-1, SPRY1) or underexpressed (TSPAN16, ADAMTSL4) in p185BCR-ABL-positive ALL relative to p210BCR-ABL-positive ALL. Finally, we constructed a gene expression- and interaction-based outcome predictor consisting of 27 genes (including GRB2, GAB1, GLI1, IRS1, RUNX2, and SPP1), which correlated with overall survival in BCR-ABL-positive adult ALL (P = .0001), independent of age (P = .25) and WBC count at presentation (P = .003).We identified prominent molecular features of BCR-ABL-positive adult ALL, which may be useful for developing novel therapeutic targets and prognostic markers in this disease.
View details for DOI 10.1200/JCO.2006.09.3534
View details for Web of Science ID 000245851900009
View details for PubMedID 17312329