Establishing Regionalized Acute Care Across a Health Care System to Decentralize Postoperative Care After Oncologic Surgery.

PURPOSE: Patients undergoing oncologic surgery at major referral centers frequently experience postdischarge care fragmentation, which has been associated with poor outcomes. This report describes and evaluates the outcomes of an intervention at Memorial Sloan Kettering Cancer Center (MSKCC) to decentralize postdischarge postoperative acute care within our health care system. METHODS: In 2018, MSKCC completed the addition of six regional acute care clinics called symptom care clinics (SCCs) to existing regional outpatient clinics. Acute care was previously only available within our system at a single centralized urgent care center (UCC). All patients undergoing surgery in our system between January 1, 2019, and June 30, 2021, were followed for 90 days. The exposure was the site of initial acute care presentation-UCC or SCC-and outcomes included utilization, access, financial toxicity, and mortality. Mortality was adjusted using hierarchical modeling at the level of the region. RESULTS: A total of 6,992 postsurgical patients experienced 10,525 acute care visits in our system within 90 days of surgery. Twenty-nine percent of these patients presented to the SCC first. These patients were older but had fewer comorbidities and shorter index length of stay compared with UCC patients. Utilization of SCCs increased substantially while UCC utilization decreased during a period of stable case volume. SCCs were closer to patients' homes, and wait times were shorter. Rates of financial toxicity were similar between groups. Of this high-risk cohort accessing acute care postoperatively, 90-day mortality was similar for UCC and SCC patients (P = .731). CONCLUSION: This model of decentralized acute care after oncologic surgery was increasingly used over time with comparable patient safety. Health systems should emphasize patient-centered care by supporting safe strategies for regionalized care even when treatments are delivered at centralized referral centers.

Use of a portable computed tomography scanner for chest imaging of COVID-19 patients in the urgent care at a tertiary cancer center.

To present a novel use of a portable computed tomography (CT) for evaluation of COVID-19 patients presenting to an urgent care center (UCC). Infection control is imperative for hospitals treating patients with COVID-19, even more so in cancer centers, where the majority of the patient population is susceptible to adverse outcomes from the infection. Over the past several weeks, our department has worked to repurpose a portable CT scanner from our surgical colleagues that operates with fixed-parameters to perform non-contrast, helical, thin-slice chest imaging to address the known pulmonary complications of COVID-19. Despite the technical limitations of the portable CT unit that was repurposed for the UCC, diagnostic-quality images in an acute care setting were successfully obtained. Repurposing of a portable CT scanner for use in COVID-19 patients offers a feasible option to obtain diagnostic quality images while minimizing the risk of exposing other patients and hospital staff to an infected patient.

Emergency room imaging in patients with genitourinary cancers: analysis of the spectrum of CT findings and their relation to patient outcomes.

PURPOSE: To assess the spectrum of computed tomography (CT) findings in patients with genitourinary cancers visiting the emergency room (ER) and evaluate the relationship between CT findings and overall survival (OS). METHODS: Retrospective analysis of consecutive patients with genitourinary cancers undergoing CT during an ER visit at a tertiary cancer center during a 20-month period. CTs were considered positive if there were findings relevant to the presenting complaint(s). Demographic/clinical variables were recorded. OS was evaluated using Kaplan-Meier curves. Univariate and multivariate Cox proportional hazards regression (HR) was used to evaluate OS predictors. RESULTS: Two hundred twenty-seven patients (243 visits) were included. The most common primary tumors were prostate (121 [49.8%]), bladder/urothelial (78 [32.1%]), and renal (69 [28.4%]). Common presenting complaints were abdominal pain (67 [27.6%]), respiratory symptoms (49 [20.2%]), neurological signs (37 [15.2%]), and fever (34 [14.0%]). CT findings were positive in 172 patients (70.8%) and included new/increased metastases (21.4% [52/243]), fluid collections (7.4% [18/243]), urinary tract infection/inflammation (6.2% [15/243]), enteritis/colitis (5.3% [13/243]), and pneumonia (4.9% [12/243]). A positive ER CT was associated with patient admission (p = 0.01). At multivariate analysis, independently predictive factors of shorter survival were positive ER CT (HR = 2.09 [95% CI 1.16-3.76, p = 0.01), hospital admission (HR = 2.17 [95% CI 1.38-3.41], p < 0.01), and recent systemic treatment (HR = 2.10 [95% CI 1.32-3.35], p < 0.01). CONCLUSION: When CT was performed, it was able to identify a structural cause for the presenting complaint in the majority of patients with genitourinary cancers attending the ER. A positive ER CT was associated with hospital admission and poorer overall survival.

T2 magnetic resonance assay for the rapid diagnosis of candidemia in whole blood: a clinical trial.

BACKGROUND: Microbiologic cultures, the current gold standard diagnostic method for invasive Candida infections, have low specificity and take up to 2-5 days to grow. We present the results of the first extensive multicenter clinical trial of a new nanodiagnostic approach, T2 magnetic resonance (T2MR), for diagnosis of candidemia. METHODS: Blood specimens were collected from 1801 hospitalized patients who had a blood culture ordered for routine standard of care; 250 of them were manually supplemented with concentrations from <1 to 100 colony-forming units (CFUs)/mL for 5 different Candida species. RESULTS: T2MR demonstrated an overall specificity per assay of 99.4% (95% confidence interval [CI], 99.1%-99.6%) with a mean time to negative result of 4.2 ± 0.9 hours. Subanalysis yielded a specificity of 98.9% (95% CI, 98.3%-99.4%) for Candida albicans/Candida tropicalis, 99.3% (95% CI, 98.7%-99.6%) for Candida parapsilosis, and 99.9% (95% CI, 99.7%-100.0%) for Candida krusei/Candida glabrata. The overall sensitivity was found to be 91.1% (95% CI, 86.9%-94.2%) with a mean time of 4.4 ± 1.0 hours for detection and species identification. The subgroup analysis showed a sensitivity of 92.3% (95% CI, 85.4%-96.6%) for C. albicans/C. tropicalis, 94.2% (95% CI, 84.1%-98.8%) for C. parapsilosis, and 88.1% (95% CI, 80.2%-93.7%) for C. krusei/C. glabrata. The limit of detection was 1 CFU/mL for C. tropicalis and C. krusei, 2 CFU/mL for C. albicans and C. glabrata, and 3 CFU/mL for C. parapsilosis. The negative predictive value was estimated to range from 99.5% to 99.0% in a study population with 5% and 10% prevalence of candidemia, respectively. CONCLUSIONS: T2MR is the first fully automated technology that directly analyzes whole blood specimens to identify species without the need for prior isolation of Candida species, and represents a breakthrough shift into a new era of molecular diagnostics. CLINICAL TRIALS REGISTRATION: NCT01752166.

Hospital readmissions after liver surgery for metastatic colorectal cancer.

BACKGROUND: Hospital readmission rates after surgery are increasingly used as a measure of quality of care. Numerous efforts to decrease these rates have been established by care providers and insurance companies. There is sparse information available regarding readmission rates after liver resection for metastatic colorectal cancer (mCRC). METHODS: Data from hospital readmissions occurring within 30 days after liver resection and/or open ablation for mCRC between 2005 and 2010 were captured from the urgent care center (emergency room) database and were compared with data from the institutional database. Complications during the primary stay and those leading to readmission were analyzed and graded with an established scoring system. The time course of complications and their therapeutic management were analyzed as well. RESULTS: Of 746 patients who underwent surgery during this period, 277 (37%) developed medical or surgical complications within 30 days, and 97 (13%) required readmission after discharge. The most common causes for readmission were perihepatic or intra-abdominal collections (40%), wound issues (13%), and gastrointestinal issues (12%). Forty-four patients had complications grade 3 or higher during readmission, thus representing 34% of all major complications (grade 3 or higher). Seventy-four readmitted patients (27% of all patients with complications) had a complication of lesser grade during their primary stay. The median postoperative day of readmission was 15 (range, 6-30) with wide variation among complication types. CONCLUSION: Readmission is common after liver resection and/or ablation for mCRC. One quarter of patients who develop complications postoperatively will have their most significant complication as an outpatient and require rehospitalization.

Probability of mortality of critically ill cancer patients at 72 h of intensive care unit (ICU) management.

GOALS: To develop and validate a model for probability of hospital mortality for cancer patients at 72 h of intensive care unit (ICU) management. PATIENTS AND METHODS: This is an inception cohort study performed at four ICUs of academic medical centers in the United States. Defined continuous and categorical variables were collected on consecutive patients with cancer admitted to the ICU. A preliminary model was developed from 827 patients and then validated on an additional 415 patients. Multiple logistic regression modeling was used to develop the models, which were subsequently evaluated for discrimination and calibration. The main outcome measure is in-hospital death. RESULTS: A probability of mortality model, which incorporates ten discrete categorical variables, was developed and validated. All variables were collected at 72 h of ICU care. Variables included evidence of disease progression, performance status before hospitalization, heart rate >100 beats/min, Glasgow coma score 40 mg/dl, and a urine output of <150 ml for any 8 h in the previous 24 h. The p values for the fit of the preliminary and validation models were 0.535 and 0.354 respectively, and the areas under the receiver operating characteristic (ROC) curves were 0.809 and 0.820. CONCLUSIONS: We report a multivariable logistic regression model to estimate the probability of hospital mortality in critically ill cancer patients at 72 h of ICU care. The model is comprised of ten unambiguous and readily available variables. When used in conjunction with clinical judgment, this model should improve discussions about goals of care of these patients. Additional validation in a community hospital setting is warranted.