Mesenteric cysts are rare, benign tumors of uncertain etiology that require management. The Müllerian subtype of one of the rarer types of mesenteric cysts. Diagnosis of a mesenteric cyst typically involves nonspecific symptoms or an asymptomatic presentation. Treatment involves resection to prevent life-threatening complications. Resection through laparotomy surgery is the most common approach. The laparoscopic approach is gaining traction. We present a case in which a large mesenteric cyst was resected using a robotic assisted laparoscopic approach.
Terminal oncology intensive care unit (ICU) hospitalizations are associated with high costs and inferior quality of care. This study identifies and characterizes potentially avoidable terminal admissions of oncology patients to ICUs.This was a retrospective case series of patients cared for in an academic medical center's ambulatory oncology practice who died in an ICU during July 1, 2012 to June 30, 2013. An oncologist, intensivist, and hospitalist reviewed each patient's electronic health record from 3 months preceding terminal hospitalization until death. The primary outcome was the proportion of terminal ICU hospitalizations identified as potentially avoidable by two or more reviewers. Univariate and multivariate analysis were performed to identify characteristics associated with avoidable terminal ICU hospitalizations.Seventy-two patients met inclusion criteria. The majority had solid tumor malignancies (71%), poor performance status (51%), and multiple encounters with the health care system. Despite high-intensity health care utilization, only 25% had documented advance directives. During a 4-day median ICU length of stay, 81% were intubated and 39% had cardiopulmonary resuscitation. Forty-seven percent of these hospitalizations were identified as potentially avoidable. Avoidable hospitalizations were associated with factors including: worse performance status before admission (median 2 v 1; P = .01), worse Charlson comorbidity score (median 8.5 v 7.0, P = .04), reason for hospitalization (P = .006), and number of prior hospitalizations (median 2 v 1; P = .05).Given the high frequency of avoidable terminal ICU hospitalizations, health care leaders should develop strategies to prospectively identify patients at high risk and formulate interventions to improve end-of-life care.
Objectives: Acute respiratory distress syndrome is frequently under recognized and associated with increased mortality. Previously, we developed a model that used machine learning and natural language processing of text from radiology reports to identify acute respiratory distress syndrome. The model showed improved performance in diagnosing acute respiratory distress syndrome when compared to a rule-based method. In this study, our objective was to externally validate the natural language processing model in patients from an independent hospital setting. Design: Secondary analysis of data across five prospective clinical studies. Setting: An urban, tertiary care, academic hospital. Patients: Adult patients admitted to the medical ICU and at-risk for acute respiratory distress syndrome. Interventions: None. Measurements and Main Results: The natural language processing model was previously derived and internally validated in burn, trauma, and medical patients at Loyola University Medical Center. Two machine learning models were examined with the following text features from qualifying radiology reports: 1) word representations ( n -grams) and 2) standardized clinical named entity mentions mapped from the National Library of Medicine Unified Medical Language System. The models were externally validated in a cohort of 235 patients at the University of Chicago Medicine, among which 110 (47%) were diagnosed with acute respiratory distress syndrome by expert annotation. During external validation, the n -gram model demonstrated good discrimination between acute respiratory distress syndrome and nonacute respiratory distress syndrome patients ( C -statistic, 0.78; 95% CI, 0.72–0.84). The n -gram model had a higher discrimination for acute respiratory distress syndrome when compared with the standardized named entity model, although not statistically significant ( C -statistic 0.78 vs 0.72; p = 0.09). The most important features in the model had good face validity for acute respiratory distress syndrome characteristics but differences in frequencies did occur between hospital settings. Conclusions: Our computable phenotype for acute respiratory distress syndrome had good discrimination in external validation and may be used by other health systems for case-identification. Discrepancies in feature representation are likely due to differences in characteristics of the patient cohorts.
'/%3e%3c/defs%3e%3cpath fill='%23012169' d='M0 0h10080v5040H0z'/%3e%3cpath stroke='%23fff' stroke-width='.6' d='m0 0 6 3m0-3L0 3' clip-path='url(%23b)' transform='scale(840)'/%3e%3cpath stroke='%23e4002b' stroke-width='.4' d='m0 0 6 3m0-3L0 3' clip-path='url(%23c)' transform='scale(840)'/%3e%3cpath stroke='%23fff' stroke-width='840' d='M2520 0v2520M0 1260h5040'/%3e%3cpath stroke='%23e4002b' stroke-width='504' d='M2520 0v2520M0 1260h5040'/%3e%3cg fill='%23fff'%3e%3cuse xlink:href='%23d' x='2520' y='3780'/%3e%3cuse xlink:href='%23a' x='7560' y='4200'/%3e%3cuse xlink:href='%23a' x='6300' y='2205'/%3e%3cuse xlink:href='%23a' x='7560' y='840'/%3e%3cuse xlink:href='%23a' x='8680' y='1869'/%3e%3cuse xlink:href='%23e' x='8064' y='2730'/%3e%3c/g%3e%3c/svg%3e)
'/%3e%3cuse xlink:href='%23a' transform='rotate(23.036 .093 25.536)'/%3e%3cuse xlink:href='%23a' transform='rotate(45.87 1.273 16.18)'/%3e%3cuse xlink:href='%23a' transform='rotate(69.945 .996 12.078)'/%3e%3cuse xlink:href='%23a' transform='rotate(20.66 -19.689 31.932)'/%3e%3c/svg%3e)
