Erector spinae plane block versus intercostal nerve block for postoperative analgesia in lung cancer surgery.

BACKGROUND: A recent trend in postoperative analgesia for lung cancer surgery relies on regional nerve blocks with decreased opioid administration. Our study aims to critically assess the continuous ultrasound-guided erector spinae plane block (ESPB) at our institution and compare it to a standard regional anesthetic technique, the intercostal nerve block (ICNB). PATIENTS AND METHODS: A prospective randomized-control study was performed to compare outcomes of patients, scheduled for video-assisted thoracoscopic (VATS) lung cancer resection, allocated to the ESPB or ICNB group. Primary outcomes were total opioid consumption and subjective pain scores at rest and cough each hour in 48 h after surgery. The secondary outcome was respiratory muscle strength, measured by maximal inspiratory and expiratory pressures (MIP/MEP) after 24 h and 48 h. RESULTS: 60 patients met the inclusion criteria, half ESPB. Total opioid consumption in the first 48 h was 21. 64 ± 14.22 mg in the ESPB group and 38.34 ± 29.91 mg in the ICNB group (p = 0.035). The patients in the ESPB group had lower numerical rating scores at rest than in the ICNB group (1.19 ± 0.73 vs. 1.77 ± 1.01, p = 0.039). There were no significant differences in MIP/MEP decrease from baseline after 24 h (MIP p = 0.088, MEP p = 0.182) or 48 h (MIP p = 0.110, MEP p = 0.645), time to chest tube removal or hospital discharge between the two groups. CONCLUSIONS: In the first 48 h after surgery, patients with continuous ESPB required fewer opioids and reported less pain than patients with ICNB. There were no differences regarding respiratory muscle strength, postoperative complications, and time to hospital discharge. In addition, continuous ESPB demanded more surveillance than ICNB.

Real-life long-term outcomes of upfront surgery in patients with resectable stage I-IIIA non-small cell lung cancer.

BACKGROUND: Treatment of early-stage non-small cell lung cancer (NSCLC) is rapidly evolving. When introducing novelties, real-life data on effectiveness of currently used treatment strategies are needed. The present study evaluated outcomes of stage I-IIIA NSCLC patients treated with upfront radical surgery in everyday clinical practice, between 2010-2017. PATIENTS AND METHODS: Data of 539 consecutive patients were retrieved from a prospective hospital-based registry. All diagnostic, treatment and follow-up procedures were performed at the same thoracic oncology centre according to the valid guidelines. The primary outcome was overall survival (OS) analysed by clinical(c) and pathological(p) TNM (tumour, node, metastases) stage. The impact of clinicopathological characteristics on OS was evaluated using univariable (UVA) and multivariable regression analysis (MVA). RESULTS: With a median follow-up of 53.9 months, median OS and 5-year OS rate in the overall population were 90.4 months and 64.4%. Five-year OS rates by pTNM stage I, II and IIIA were 70.2%, 60.21%, and 49.9%, respectively. Both cTNM and pTNM stages were associated with OS; but only pTNM retained its independent prognostic value (p = 0.003) in MVA. Agreement between cTNM and pTNM was 69.0%. Next to pTNM, age (p = 0.001) and gender (p = 0.004) retained their independent prognostic value for OS. CONCLUSIONS: The study showed favourable outcomes of resectable stage I-IIIA NSCLC treated with upfront surgery in real-life. Relatively low agreement between cTNM and pTNM stages and independent prognostic value of only pTNM, observed in real-life data, suggest that surgery remains the most accurate provider of the anatomical stage of disease and important upfront therapy.

Feasibility of spirography features for objective assessment of motor function in Parkinson's disease.

OBJECTIVE: Parkinson's disease (PD) is currently incurable, however proper treatment can ease the symptoms and significantly improve the quality of life of patients. Since PD is a chronic disease, its efficient monitoring and management is very important. The objective of this paper was to investigate the feasibility of using the features and methodology of a spirography application, originally designed to detect early Parkinson's disease (PD) motoric symptoms, for automatically assessing motor symptoms of advanced PD patients experiencing motor fluctuations. More specifically, the aim was to objectively assess motor symptoms related to bradykinesias (slowness of movements occurring as a result of under-medication) and dyskinesias (involuntary movements occurring as a result of over-medication). MATERIALS AND METHODS: This work combined spirography data and clinical assessments from a longitudinal clinical study in Sweden with the features and pre-processing methodology of a Slovenian spirography application. The study involved 65 advanced PD patients and over 30,000 spiral-drawing measurements over the course of three years. Machine learning methods were used to learn to predict the "cause" (bradykinesia or dyskinesia) of upper limb motor dysfunctions as assessed by a clinician who observed animated spirals in a web interface. The classification model was also tested for comprehensibility. For this purpose a visualisation technique was used to present visual clues to clinicians as to which parts of the spiral drawing (or its animation) are important for the given classification. RESULTS: Using the machine learning methods with feature descriptions and pre-processing from the Slovenian application resulted in 86% classification accuracy and over 0.90 AUC. The clinicians also rated the computer's visual explanations of its classifications as at least meaningful if not necessarily helpful in over 90% of the cases. CONCLUSIONS: The relatively high classification accuracy and AUC demonstrates the usefulness of this approach for objective monitoring of PD patients. The positive evaluation of computer's explanations suggests the potential use of this methodology in a decision support setting.

Automatic Spiral Analysis for Objective Assessment of Motor Symptoms in Parkinson's Disease.

A challenge for the clinical management of advanced Parkinson's disease (PD) patients is the emergence of fluctuations in motor performance, which represents a significant source of disability during activities of daily living of the patients. There is a lack of objective measurement of treatment effects for in-clinic and at-home use that can provide an overview of the treatment response. The objective of this paper was to develop a method for objective quantification of advanced PD motor symptoms related to off episodes and peak dose dyskinesia, using spiral data gathered by a touch screen telemetry device. More specifically, the aim was to objectively characterize motor symptoms (bradykinesia and dyskinesia), to help in automating the process of visual interpretation of movement anomalies in spirals as rated by movement disorder specialists. Digitized upper limb movement data of 65 advanced PD patients and 10 healthy (HE) subjects were recorded as they performed spiral drawing tasks on a touch screen device in their home environment settings. Several spatiotemporal features were extracted from the time series and used as inputs to machine learning methods. The methods were validated against ratings on animated spirals scored by four movement disorder specialists who visually assessed a set of kinematic features and the motor symptom. The ability of the method to discriminate between PD patients and HE subjects and the test-retest reliability of the computed scores were also evaluated. Computed scores correlated well with mean visual ratings of individual kinematic features. The best performing classifier (Multilayer Perceptron) classified the motor symptom (bradykinesia or dyskinesia) with an accuracy of 84% and area under the receiver operating characteristics curve of 0.86 in relation to visual classifications of the raters. In addition, the method provided high discriminating power when distinguishing between PD patients and HE subjects as well as had good test-retest reliability. This study demonstrated the potential of using digital spiral analysis for objective quantification of PD-specific and/or treatment-induced motor symptoms.

Elicitation of neurological knowledge with argument-based machine learning.

OBJECTIVE: The paper describes the use of expert's knowledge in practice and the efficiency of a recently developed technique called argument-based machine learning (ABML) in the knowledge elicitation process. We are developing a neurological decision support system to help the neurologists differentiate between three types of tremors: Parkinsonian, essential, and mixed tremor (comorbidity). The system is intended to act as a second opinion for the neurologists, and most importantly to help them reduce the number of patients in the "gray area" that require a very costly further examination (DaTSCAN). We strive to elicit comprehensible and medically meaningful knowledge in such a way that it does not come at the cost of diagnostic accuracy. MATERIALS AND METHODS: To alleviate the difficult problem of knowledge elicitation from data and domain experts, we used ABML. ABML guides the expert to explain critical special cases which cannot be handled automatically by machine learning. This very efficiently reduces the expert's workload, and combines expert's knowledge with learning data. 122 patients were enrolled into the study. RESULTS: The classification accuracy of the final model was 91%. Equally important, the initial and the final models were also evaluated for their comprehensibility by the neurologists. All 13 rules of the final model were deemed as appropriate to be able to support its decisions with good explanations. CONCLUSION: The paper demonstrates ABML's advantage in combining machine learning and expert knowledge. The accuracy of the system is very high with respect to the current state-of-the-art in clinical practice, and the system's knowledge base is assessed to be very consistent from a medical point of view. This opens up the possibility to use the system also as a teaching tool.