Caroline Putzke

The reliability and validity of the upper lip bite test compared with the Mallampati classification to predict difficult laryngoscopy: an external prospective evaluation.

Recently, a new bedside screening test to predict the occurrence of a difficult laryngoscopy has been developed as a substitute for the Mallampati classification. The Upper-Lip-Bite test (ULBT) evaluated the patient’s ability to reach or completely cover the upper lip with the lower incisors. It is often accepted that new predictive tools should undergo an external evaluation before the tool is used in clinical practice. Thus, we evaluated this test with respect to applicability, interobserver reliability, and discriminating power and compared it with the Mallampati-score (using Samsoon and Young’s modification). The ULBT could not be applied in 12% of all patients (Mallampati score, <1%). However, the interobserver reliability was better for the ULBT (&kgr; = 0.79 versus &kgr; = 0.59). The discriminating power to predict a patient with difficult laryngoscopy was evaluated in 1425 consecutive patients. Both tests were assessed simultaneously in these patients by two specially trained independent observers. After the induction of anesthesia, the laryngoscopic view was assessed by the attending anesthesiologist using the classification of Cormack and Lehane. A grade I or II was called easy laryngoscopy and grade III and IV difficult laryngoscopy. The discriminating power for both tests was low (0.60 for the ULBT [95% confidence interval, 0.57–0.63] and 0.66 [0.63–0.69]) for the Mallampati score), indicating that both tests are poor predictors as single screening tests.

Physical therapy and active exercises--an adequate treatment for prevention of late whiplash syndrome? Randomized controlled trial in 200 patients.

Abstract The aim of this study was to compare the effect of a physical therapy regimen including active exercises with the current standard treatment on reduction of pain 6 weeks and 6 months after whiplash injury caused by motor vehicle collision. Two hundred patients were enrolled in a prospective randomized controlled trial. In the standard group, treatment consisted of immobilization with a soft collar over 7 days. In the physical therapy group, patients were scheduled for 10 physical therapy appointments including active exercises within 14 days after enrollment. Pain intensity was rated by all patients daily during the first week, the sixth week, and 6 months after recruitment, using a numeric rating scale (0–10). Data analyses were performed by comparing the mean (over 1 week) pain scores between the two different treatment groups. Ninety‐seven patients were randomly assigned to the standard treatment group and 103 to the physical therapy group. During the first week, there was no significant difference in mean pain intensity between the standard treatment group (4.76 ± 2.15) and the physical therapy group (4.36 ± 2.14). However, after 6 weeks, mean pain intensity was significantly (p = 0.002) lower in the physical therapy group (1.49 ± 2.26 versus 2.7 ± 2.78). Similarly, after 6 months, significantly (p < 0.001) less pain was reported in the physical therapy group (1.17 ± 2.13) than the standard treatment group (2.33 ± 2.56). We conclude that a physical therapy regimen which includes active exercises is superior in reducing pain 6 weeks and 6 months after whiplash injury compared to the current standard treatment with a soft collar.

Vorhersage von Übelkeit und Erbrechen in der postoperativen Phase durch ein künstliches neuronales Netz

ZusammenfassungFragestellungÜbelkeit und Erbrechen in der postoperativen Phase (PONV) sind nach wie vor häufige und subjektiv sehr unangenehme Nebenwirkungen einer Narkose. Dennoch sollte eine antiemetische Prophylaxe nur bei Risikopatienten durchgeführt werden, die durch entsprechende Vorhersagemodelle identifiziert werden müssen. Alle traditionellen Risikoscores basieren auf den Ergebnissen logistischer Regressionsanalysen. Alternativ kann aber auch ein künstliches neuronales Netz (KNN) für solche Vorhersagen eingesetzt werden, mit dem sich komplexe und nichtlineare Zusammenhänge gut modellieren lassen. Es wird die Entwicklung eines solchen KNN zur PONV-Vorhersage vorgestellt und dessen Prognosegenauigkeit mit der zweier vereinfachter Risikomodelle (Apfel- und Koivuranta-Score) verglichen.MethodikGrundlage für die Entwicklung des KNN waren die Daten von 1.764 Patienten, die sich einer elektiven Operation in balancierter Allgemeinanästhesie unterzogen hatten. Das Training der KNN erfolgte an 1.364 Datensätzen, mit weiteren 400 wurde es überwacht. Das KNN mit der besten Vorhersagegenauigkeit wurde im nächsten Schritt mit den etablierten Risikomodellen an weiteren 400 externen Patientendaten verglichen. Bewertet wurden Diskrimination (gemessen anhand der Fläche unter einer Receiver-operating-characteristic-Kurve) und Kalibration (gemessen anhand der Ausgleichsgerade einer gewichteten linearen Korrelation zwischen vorhergesagter und tatsächlicher PONV-Inzidenz) sowie die praktische Anwendbarkeit.ErgebnisseDie Diskriminationsfähigkeit des KNN war mit 0,74 (95%-Konfidenzintervall: 0,70–0,78) signifikant besser (p<0,0001) als beim vereinfachten Score von Apfel (0,66; 95%-KI: 0,61–0,71) oder Koivuranta (0,69; 95%-KI: 0,65–0,74). Die Übereinstimmung zwischen der vorhergesagten und der tatsächlichen PONV-Inzidenz war beim KNN ebenfalls am besten. Die Ausgleichsgerade des KNN kam den Anforderungen an einen idealen Verlauf (y=1,0x+0) sehr nahe (KNN: y=1,11x+0; Apfel: y=0,71x+1; Koivuranta: 0,86x−5).SchlussfolgerungDie Verbesserung der PONV-Prognose durch das KNN ist klinisch relevant. Dennoch überwiegen die praktischen Nachteile eines solchen Systems, das nicht ohne Rechnerunterstützung angewandt werden kann. Wegen der einfachen Handhabung in der klinischen Praxis empfehlen wir weiterhin die Anwendung eines der beiden vereinfachten Vorhersagemodelle.SummaryObjectivePostoperative nausea and vomiting (PONV) are still frequent side-effects after general anaesthesia. These unpleasant symptoms for the patients can be sufficiently reduced using a multimodal antiemetic approach. However, these efforts should be restricted to risk patients for PONV. Thus, predictive models are required to identify these patients before surgery. So far all risk scores to predict PONV are based on results of logistic regression analysis. Artificial neural networks (ANN) can also be used for prediction since they can take into account complex and non-linear relationships between predictive variables and the dependent item. This study presents the development of an ANN to predict PONV and compares its performance with two established simplified risk scores (Apfels and Koivurantas scores).MethodsThe development of the ANN was based on data from 1,764 patients undergoing elective surgical procedures under balanced anaesthesia. The ANN was trained with 1,364 datasets and a further 400 were used for supervising the learning process. One of the 49 ANNs showing the best predictive performance was compared with the established risk scores with respect to practicability, discrimination (by means of the area under a receiver operating characteristics curve) and calibration properties (by means of a weighted linear regression between the predicted and the actual incidences of PONV).ResultsThe ANN tested showed a statistically significant (p<0.0001) and clinically relevant higher discriminating power (0.74; 95% confidence interval: 0.70–0.78) than the Apfel score (0.66; 95% CI: 0.61–0.71) or Koivurantas score (0.69; 95% CI: 0.65–0.74). Furthermore, the agreement between the actual incidences of PONV and those predicted by the ANN was also better and near to an ideal fit, represented by the equation y=1.0x+0. The equations for the calibration curves were: KNN y=1.11x+0, Apfel y=0.71x+1, Koivuranta 0.86x−5.ConclusionThe improved predictive accuracy achieved by the ANN is clinically relevant. However, the disadvantages of this system prevail because a computer is required for risk calculation. Thus, we still recommend the use of one of the simplified risk scores for clinical practice.

Cost analyses of remifentanil, mivacurium and ropivacaine - a systematic review.

Remifentanil, mivacurium and ropivacaine are the latest innovations in clinical anaesthesia and have gained increasing importance in daily practise due to their unique pharmacodynamic and pharmacokinetic properties. However, drug acquisition costs for these agents are considerably higher in most countries than for comparable substances. This review provides a systematic, critical appraisal of pharmacoeconomic studies with remifentanil, mivacurium and ropivacaine, primarily based on prospective, randomised trials. Results from analyses using cost-minimising techniques stress the issue of the higher drug acquisition costs. However, studies using a more sophisticated method (e.g., cost-effectiveness analysis) indicate comparable costs or even financial advantage in favour of the newer investigative drugs remifentanil, mivacurium and ropivacaine.