Precise nerve localization is of major importance in both surgery and regional anesthesia. Optically based techniques can identify tissue through differences in optical properties, like absorption and scattering. The aim of this study was to evaluate the potential of optical spectroscopy (diffuse reflectance spectroscopy) for clinical nerve identification in vivo. Eighteen patients (8 male, 10 female, age 53 ± 13 years) undergoing inguinal lymph node resection or resection or a soft tissue tumor in the groin were included to measure the femoral or sciatic nerve and the surrounding tissues. In vivo optical measurements were performed using Diffuse Reflectance Spectroscopy (400–1600 nm) on nerve, near nerve adipose tissue, muscle, and subcutaneous fat using a needle-shaped probe. Model-based analyses were used to derive verified quantitative parameters as concentrations of optical absorbers and several parameters describing scattering. A total of 628 optical spectra were recorded. Measured spectra reveal noticeable tissue specific characteristics. Optical absorption of water, fat, and oxy- and deoxyhemoglobin was manifested in the measured spectra. The parameters water and fat content showed significant differences (P < 0.005) between nerve and all surrounding tissues. Classification using k-Nearest Neighbor based on the derived parameters revealed a sensitivity of 85% and a specificity of 79%, for identifying nerve from surrounding tissues. Diffuse Reflectance Spectroscopy identifies peripheral nerve bundles. The differences found between tissue groups are assignable to the tissue composition and structure.

Objective: To identify, on the basis of past performance, those hospitals that demonstrate good outcomes in sufficient numbers to make it likely that they will provide adequate quality of care in the future, using a combined measure of volume and outcome (CM-V&O). To compare this CM-V&O with measures using outcome-only (O-O) or volume-only (V-O), and verify 2010-quality of care assessment on 2011 data. Design: Secondary analysis of clinical audit data. Setting: The Dutch Surgical Colorectal Audit database of 2010 and 2011, the Netherlands. Participants: 8911 patients (test population, treated in 2010) and 9212 patients (verification population, treated in 2011) who underwent a resection of primary colorectal cancer in 89 Dutch hospitals. Main Outcome Measures: Outcome was measured by Observed/Expected (O/E) postoperative mortality and morbidity. CM-V&O states 2 criteria; 1) outcome is not significantly worse than average, and 2) outcome is significantly better than substandard, with 'substandard care' being defined as an unacceptably high O/E threshold for mortality and/or morbidity (which we set at 2 and 1.5 respectively). Results: Average mortality and morbidity in 2010 were 4.1 and 24.3% respectively. 84 (94%) hospitals performed 'not worse than average' for mortality, but only 21 (24%) of those were able to prove they were also 'better than substandard' (O/E<2). For morbidity, 42 hospitals (47%) met the CM-V&O. Morbidity in 2011 was significantly lower in these hospitals (19.8 vs. 22.8% p<0.01). No relationship was found between hospitals' 2010 performance on O-O en V-O, and the quality of their care in 2011. Conclusion: CM-V&O for morbidity can be used to identify hospitals that provide adequate quality and is associated with better outcomes in the subsequent year.

Objective: The aim of the study was to identify risk factors for postoperative mortality in patients undergoing surgery for colon cancer. We looked specifically at patients ≥ 80 years of age in whom a nonelective colon cancer resection was performed. Study Design: Observational study. Methods: We included data from 6,161 patients who underwent colon cancer surgery in 2010 in a Dutch hospital; a nonelective colon cancer resection was performed in 1,172 of these patients. Risk factors for postoperative mortality were identified using a multivariate logistic regression analysis. We studied elective and nonelective intestinal resections separately in different age groups. Results: Mortality in the total study population was 4.9%. Mortality increased with age in patients who underwent either elective or nonelective intestinal resection. For patients ≥ 80 years of age who underwent nonelective intestinal resection, each additional risk factor doubled the mortality risk. In patients aged ≥ 80 years with an American Society of Anesthesiologists classification of class ≥ 3 who underwent a left hemicolectomy or 'other' intestinal resection, the postoperative mortality rate was 41%; in patients of the same age without additional risk factors this was 7%. CONCLUSIONS In patients ≥ 80 years of age with 2 or more additional risk factors, nonelective intestinal resection should be considered a high-risk procedure with a mortality risk of up to 41%. This result can be used in clinical decision making concerning treatment and in providing information for patients and their families.

Aims: The aim of the study was to assess which factors contribute to postoperative mortality, especially in elderly patients who undergo emergency colon cancer resections, using a nationwide population-based database. Methods: 6,161 patients (1,172 nonelective) who underwent a colon cancer resection in 2010 in the Netherlands were included. Risk factors for postoperative mortality were investigated using a multivariate logistic regression model for different age groups, elective and nonelective patients separately. Results: For both elective and nonelective patients, mortality risk increased with increasing age. For nonelective elderly patients (80+ years), each additional risk factor increased the mortality risk. For a nonelective patient of 80+ years with an American Society of Anesthesiologists score of III+ and a left hemicolectomy or extended resection, postoperative mortality rate was 41% compared with 7% in patients without additional risk factors. Conclusions: For elderly patients with two or more additional risk factors, a nonelective resection should be considered a high-risk procedure with a mortality risk of up to 41%. The results of this study could be used to adequately inform patient and family and should have consequences for composing an operative team.

OBJECTIVE: To investigate the safety of laparoscopic colorectal cancer resections in a nationwide population-based study. BACKGROUND: Although laparoscopic techniques are increasingly used in colorectal cancer surgery, little is known on results outside trials. With the fast introduction of laparoscopic resection (LR), questions were raised about safety. METHODS: Of all patients who underwent an elective colorectal cancer resection in 2010 in the Netherlands, 93% were included in the Dutch Surgical Colorectal Audit. Short-term outcome after LR, open resection (OR), and converted LR were compared in a generalized linear mixed model. We further explored hospital differences in LR and conversion rates. RESULTS: A total of 7350 patients, treated in 90 hospitals, were included. LR rate was 41% with a conversion rate of 15%. After adjustment for differences in case-mix, LR was associated with a lower risk of mortality (odds ratio 0.63, P < 0.01), major morbidity (odds ratio 0.72, P < 0.01), any complications (odds ratio 0.74, P < 0.01), hospital stay more than 14 days (odds ratio 0.71, P < 0.01), and irradical resections (odds ratio 0.68, P < 0.01), compared to OR. Outcome after conversion was similar to OR (P > 0.05). A large variation in LR and conversion rates among hospitals was found; however, the difference in outcome associated with operative techniques was not influenced by hospital of treatment. CONCLUSIONS: Use of laparoscopic techniques in colorectal cancer surgery in the Netherlands is safe and results are better in short-term outcome than open surgery, irrespective of the hospital of treatment. Outcome after conversion was similar to OR.

Aims: We propose a summarizing measure for outcome indicators, representing the proportion of patients for whom all desired short-term outcomes of care (a 'textbook outcome') is realized. The aim of this study was to investigate hospital variation in the proportion of patients with a 'textbook outcome' after colon cancer resections in the Netherlands. Methods: Patients who underwent a colon cancer resection in 2010 in the Netherlands were included in the Dutch Surgical Colorectal Audit. A textbook outcome was defined as hospital survival, radical resection, no reintervention, no ostomy, no adverse outcome and a hospital stay < 14 days. We calculated the number of hospitals with a significantly higher (positive outlier) or lower (negative outlier) Observed/Expected (O/E) textbook outcome than average. As quality measures may be more discriminative in a low-risk population, analyses were repeated for low-risk patients only. Results: A total of 5582 patients, treated in 82 hospitals were included. Average textbook outcome was 49% (range 26-71%). Eight hospitals were identified as negative outliers. In these hospitals a 'textbook outcome' was realized in 35% vs. 52% in average hospitals (p < 0.01). In a sub-Analysis for low-risk patients, only one additional negative outlier was identified. Conclusions: The textbook outcome, representing the proportion of patients with a perfect hospitalization, gives a simple comprehensive summary of hospital performance, while preventing indicator driven practice. Therewith the 'textbook outcome' is meaningful for patients, providers, insurance companies and healthcare inspectorate.

Objective: To determine if composite measures based on process indicators are consistent with short-term outcome indicators in surgical colorectal cancer care. Design: Longitudinal analysis of consistency between composite measures based on process indicators and outcome indicators for 85 Dutch hospitals. Setting: The Dutch Surgical Colorectal Audit database, the Netherlands. Participants: 4732 elective patients with colon carcinoma and 2239 with rectum carcinoma treated in 85 hospitals were included in the analyses. Main outcome measures: All available process indicators were aggregated into five different composite measures. The association of the different composite measures with risk-adjusted postoperative mortality and morbidity was analysed at the patient and hospital level. Results: At the patient level, only one of the composite measures was negatively associated with morbidity for rectum carcinoma. At the hospital level, a strong negative association was found between composite measures and hospital mortality and morbidity rates for rectum carcinoma (p<0.05), and hospital morbidity rates for colon carcinoma. Conclusions: For individual patients, a high score on the composite measures based on process indicators is not associated with better short-term outcome. However, at the hospital level, a good score on the composite measures based on process indicators was consistent with more favourable risk-adjusted short-term outcome rates.

Aims: The purpose of this study was to determine how expected mortality based on case-mix varies between colorectal cancer patients treated in non-teaching, teaching and university hospitals, or high, intermediate and low-volume hospitals in the Netherlands. Material and methods: We used the database of the Dutch Surgical Colorectal Audit 2010. Factors predicting mortality after colon and rectum carcinoma resections were identified using logistic regression models. Using these models, expected mortality was calculated for each patient. Results: 8580 patients treated in 90 hospitals were included in the analysis. For colon carcinoma, hospitals' expected mortality ranged from 1.5 to 14%. Average expected mortality was lower in patients treated in high-volume hospitals than in low-volume hospitals (5.0 vs. 4.3%, p < 0.05). For rectum carcinoma, hospitals expected mortality varied from 0.5 to 7.5%. Average expected mortality was higher in patients treated in non-teaching and teaching hospitals than in university hospitals (2.7 and 2.3 vs. 1.3%, p < 0.01). Furthermore, rectum carcinoma patients treated in high-volume hospitals had a higher expected mortality than patients treated in low-volume hospitals (2.6 vs. 2.2% p < 0.05). We found no differences in risk-adjusted mortality. Conclusions: High-risk patients are not evenly distributed between hospitals. Using the expected mortality as an integrated measure for case-mix can help to gain insight in where high-risk patients go. The large variation in expected mortality between individual hospitals, hospital types and volume groups underlines the need for risk-adjustment when comparing hospital performances.