Justin J. Baker

Factors influencing readmission after pancreaticoduodenectomy: a multi-institutional study of 1302 patients.

Objective and Background:Morbidity, mortality, and length of hospital stay after pancreaticoduodenectomy (PD) have significantly decreased over recent decades. Despite this progress, early readmission rates after PD have been reported as high as 50%. Few reports have delineated factors associated with readmission after PD. Methods:The medical records of 6 high-volume institutions were reviewed for patients who underwent PD between 2005 and 2010. Data collection included patient characteristics, medical comorbidities, and perioperative factors. Analysis included readmissions up to 90 days after PD. Results:A total of 1302 patients underwent PD across all institutions. The 30-day and 90-day readmission rates were 15% and 19%, respectively. The most common reasons for 30-day readmission included infectious complications (n = 65) and delayed gastric emptying (n = 29). The most common reasons for readmission after 90 days included wound infections and intra-abdominal abscess (n = 75) and failure to thrive (n = 38). On multivariate analysis, factors associated with higher readmission rates included a preoperative diagnosis of chronic pancreatitis, higher transfusion requirements, and postoperative complications including intra-abdominal abscess and pancreatic fistula (all P < 0.02). Factors not associated with higher readmission rates included advanced age, body mass index, cardiovascular/pulmonary comorbidities, diabetes, steroid use, Whipple type (standard vs pylorus preserving PD), preoperative endobiliary stenting, and vascular reconstruction. Conclusions:These multi-institutional data represent a large experience of PD without the biases typically of single center studies. Factors related to infection, nutritional status, and delayed gastric emptying were the most common reasons for readmission after PD. Postoperative complications including pancreatic fistula predicted higher rates of readmission.

Continuous Detection and Decoding of Dexterous Finger Flexions With Implantable MyoElectric Sensors

A rhesus monkey was trained to perform individuated and combined finger flexions of the thumb, index, and middle finger. Nine implantable myoelectric sensors (IMES) were then surgically implanted into the finger muscles of the monkeys forearm, without any adverse effects over two years postimplantation. Using an inductive link, EMG was wirelessly recorded from the IMES as the monkey performed a finger flexion task. The EMG from the different IMES implants showed very little cross correlation. An offline parallel linear discriminant analysis (LDA) based algorithm was used to decode finger activity based on features extracted from continuously presented frames of recorded EMG. The offline parallel LDA was run on intraday sessions as well as on sessions where the algorithm was trained on one day and tested on following days. The performance of the algorithm was evaluated continuously by comparing classification output by the algorithm to the current state of the finger switches. The algorithm detected and classified seven different finger movements, including individual and combined finger flexions, and a no-movement state (chance performance = 12.5%) . When the algorithm was trained and tested on data collected the same day, the average performance was 43.8±3.6% n=10. When the training-testing separation period was five months, the average performance of the algorithm was 46.5±3.4% n=8. These results demonstrated that using EMG recorded and wirelessly transmitted by IMES offers a promising approach for providing intuitive, dexterous control of artificial limbs where human patients have sufficient, functional residual muscle following amputation.

Routine restaging PET/CT and detection of initial recurrence in sentinel lymph node positive stage III melanoma

BACKGROUND Follow-up of patients with sentinel lymph node-positive stage III melanoma uses history, physical exam, and cross-sectional imaging. The aim of this study was to evaluate positron emission tomographic (PET)/computed tomographic (CT) scans in the detection of recurrence. METHODS From 2003 to 2009, a single-institution prospective database of all cutaneous melanoma patients was used to identify sentinel lymph node-positive stage III patients with disease-free survival >1 year and 1 restaging PET/CT scan. RESULTS Thirty-eight patients were identified, with a median follow-up period of 27.5 months. Seven (18%) developed recurrence (median time to recurrence, 25 months). Recurrences were detected as follows: 3 by patients, 1 by physician, 1 by PET/CT scan and lactate dehydrogenase, 1 by PET/CT scan, and 1 by brain magnetic resonance imaging. One hundred eight follow-up PET/CT scans were performed. Two of 38 patients had asymptomatic metastases detected by routine restaging PET/CT scan, and there were 9 scans with false-positive results. CONCLUSIONS With short follow-up, the utility of routine PET/CT scans in identifying unsuspected recurrence in patients with sentinel lymph node-positive stage III melanoma appears minimal.

Multi-scale recordings for neuroprosthetic control of finger movements

We trained a rhesus monkey to perform individuated and combined finger flexions and extensions of the thumb, index, and middle finger. A Utah Electrode Array (UEA) was implanted into the hand region of the motor cortex contralateral to the monkey’s trained hand. We also implanted a microwire electrocorticography grid (µECoG) epidurally so that it covered the UEA. The µECoG grid spanned the arm and hand regions of both the primary motor and somatosensory cortices. Previously this monkey had Implantable MyoElectric Sensors (IMES) surgically implanted into the finger muscles of the monkey’s forearm. Action potentials (APs), local field potentials (LFPs), and µECoG signals were recorded from wired head-stage connectors for the UEA and µECoG grids, while EMG was recorded wirelessly. The monkey performed a finger flexion/extension task while neural and EMG data were acquired. We wrote an algorithm that uses the spike data from the UEA to perform a real-time decode of the monkey’s finger movements. Also, analyses of the LFP and µECoG data indicate that these data show trial-averaged differences between different finger movements, indicating the data are potentially decodeable.

Decoding individuated finger flexions with Implantable MyoElectric Sensors

We trained a rhesus monkey to perform randomly cued, individuated finger flexions of the thumb, index, and middle finger. Nine Implantable MyoElectric Sensors (IMES) were then surgically implanted into the finger muscles of the monkeys forearm, without any observable adverse chronic effects. Using an inductive link, we wirelessly recorded EMG from the IMES as the monkey performed a finger flexion task. A principal components analysis (PCA) based algorithm was used to decode which finger switch was pressed based on the recorded EMG. This algorithm correctly decoded which finger was moved 89% of the time. These results demonstrate that IMES offer a safe and highly promising approach for providing intuitive, dexterous control of artificial limbs and hands after amputation.

Decoding Dexterous Finger Movements in a Neural Prosthesis Model Approaching Real-World Conditions

Dexterous finger movements can be decoded from neuronal action potentials acquired from a nonhuman primate using a chronically implanted Utah Electrode Array. We have developed an algorithm that can, after training, detect and classify individual and combined finger movements without any a priori knowledge of the data, task, or behavior. The algorithm is based on changes in the firing rates of individual neurons that are tuned for one or more finger movement types. Nine different movement types, which consisted of individual flexions, individual extensions, and combined flexions of the thumb, index finger, and middle finger, were decoded. The algorithm performed reliably on data recorded continuously during movement tasks, including a no-movement state, with an overall average sensitivity and specificity that were both >;92%. These results demonstrate a viable algorithm for decoding dexterous finger movements under conditions similar to those required for a real-world neural prosthetic application.

Detection and classification of multiple finger movements using a chronically implanted Utah Electrode Array

The ability to detect and classify individual and combined finger movements from neural data is rapidly advancing. The work that has been done has demonstrated the feasibility of decoding finger movements from acutely recorded neurons. There is a need for a recording model that meets the chronic requirements of a neuroprosthetic application and to address this need we have developed an algorithm that can detect and classify individual and combined finger movements using neuronal data acquired from a chronically implanted Utah Electrode Array (UEA). The algorithm utilized the firing rates of individual neurons and performed with an average sensitivity and an average specificity that were both greater than 92% across all movement types. These results lend further support that a chronically implanted UEA is suitable for acquiring and decoding neuronal data and also demonstrate a decoding method that can detect and classify finger movements without any a priori knowledge of the data, task, or behavior.

Prognostic significance of tumor mitotic rate in T2 melanoma staged with sentinel lymphadenectomy.

Tumor mitotic rate (TMR) is an important prognostic variable for patients with thin melanoma. However it remains unclear what the significance of TMR is for more deeply invasive melanoma pathologically staged with a sentinel lymph node biopsy. We sought to determine the prognostic value of TMR in clinically node‐negative T2 melanoma patients staged with sentinel lymphadenectomy.

KRAS mutation as a prognostic factor in patients undergoing hepatic resection for metastatic colorectal cancer.

444 Background: It is well known that KRAS mutations limit the efficacy of anti-EGFR therapy in patients with metastatic colorectal cancer (mCRC). However the role of KRAS mutations in patients who undergo a curative liver resection for mCRC is less clear. The purpose of our study was to evaluate the relationship between KRAS mutation status and survival in this patient population. METHODS We examined an IRB approved tissue repository and retrospective database of 129 patients from 1998-2010 who underwent curative liver resection for mCRC. Tumors were sequenced for KRAS codons 12, 13, and 61 mutations using pyrosequencing. Overall survival (OS) and disease-free survival (DFS) were analyzed using the Kaplan-Meier method and compared using the log-rank test. Multivariate analysis was performed using the Cox proportional hazards regression method. RESULTS The median follow-up for our cohort was 20.4mo (0.4-112). Mean age was 61.4±12.3. Prior to surgical resection 55 (43%) patients received chemotherapy. 35 (27%) tumors were KRAS mutant (mt), 83 (64%) were wild-type (wt), and 11 (9%) were not characterized. Median OS for KRAS wt patients was 40.3mo vs. 27.1mo for KRAS mt patients (p=0.046). Median DFS for KRAS wt was 13.6mo vs. 7.7mo for KRAS mt patients (p=0.037). 8 patients received cetuximab post-operatively. Cetuximab status was unknown in 50 patients. When we excluded those treated with cetuximab, the median OS was 40mo for KRAS wt vs. 25mo for KRAS mt patients (p=0.007). There were no differences in OS or DFS in patients who received cetuximab (p=0.7). In a multivariable model with pre-operative chemotherapy (p=0.2), extent of resection (p=0.053), and cetuximab therapy (p=0.7), the presence of KRAS mutation was independently associated with poor prognosis (HR=2.7 [1.3-5.5]). CONCLUSIONS In patients undergoing curative liver resection for mCRC, KRAS mutation status is independently predictive of a worse outcome regardless of cetuximab therapy. KRAS status may be associated with more aggressive tumor biology. Our data supports the critical need to define KRAS mutation status and to develop therapies against KRAS and its downstream effectors.

Corrigendum: Feature Selection Methods for Robust Decoding of Finger Movements in a Non-human Primate

[This corrects the article on p. 22 in vol. 12, PMID: 29467602.].