Our study indicates that all patients with ALS have the potential to suffer from pain, the intensity of which increases with decreased functional status.

ObjectiveThe aim of this study was to prevent drug-related medication errors in the operating room by clarifying the association between the medication error category with related drugs and contributing factors.MethodsWe used data from the Japan Council for Quality Health Care’s open database on the web. We researched the medication error category, related drugs, and contributing factors. We classified each medication error category into case groups and other medication error categories into control groups. We compared the medication error factors of the 2 groups using multivariate logistic regression analysis on the medication error factors.ResultsThe total number of analyzed cases was 541. Incorrect dose was the most common medication error category in 170 cases, followed by incorrect drug in 152 cases. Medication error factors (odds ratio, 95% confidence interval) that were found to be significantly positively associated with incorrect dose were “pressor drugs” (3.0, 1.4–6.4), “anesthesia-inducing drugs” (6.3, 1.7–23.4), “lack of knowledge” (2.0, 1.3–3.3), and “drug administration” (3.4, 1.6–7.4). The medication error factors that were found to be significantly positively associated with incorrect drug were “preparation” (5.7, 3.1–10.5) and “medication passed or picked up” (102.2, 35.7–292.8).ConclusionsMedication errors are frequently occurring during drug preparation and administration in the operating room. Medical staff should thoroughly learn about operating room–specific drugs and closely monitor every step of the drug preparation and administration process. It is also important to create a workflow and improve the environment so that it reduces the likelihood of medication errors.

The most frequently reported medical incidents were drug-related and made by nurses. Excessive dosing can cause adverse reactions and possibly lead to patient deaths. On the other hand, underdosing can delay treatment and prolong hospitalization. We investigated drugs associated with and factors leading to excessive dosing or underdosing incidents to clarify when pharmacists should intervene to reduce medication-related incidents. We analyzed incident reports collected by the Japan Council for Quality Health Care between January 2009 and June 2015. In total, we found 3,024 cases of excessive dosing and 2,119 cases of underdosing. In the excessive dosing group, dosing errors and the administration of an excessive dose without an order to do so comprised 785 cases and 482 cases, respectively. In the underdosing group, there were 902 cases of dosing errors and 366 cases where the dose was discontinued too early. We used logistic-regression analysis to compare cases of causative drugs and incident factors with dosing errors and other medical incidents. Our analysis revealed that there was a significant association between steroids, narcotic analgesics, antibacterial drugs and both excessive dosing and underdosing incidents. Also, there was a significant association between nurses not confirming the correct dose and misdosing incidents. It is easy for dosing errors to occur with the aforementioned drugs because the correct dosage varies with the patient s age, renal function, overall condition, and test results. These findings suggest that pharmacists in hospital wards need to check the correct dosage before administering the medication to prevent dosing errors.

The purpose of this study was to develop and validate estimate equations for preventing adverse drug reactions (ADRs). We conductedˆve case-control studies to identify individual risk factors and subjective symptoms associated with the followingˆve ADRs: drug-induced ischemic heart disease; renal damage; muscle disorder; interstitial pneumonia; and leucopenia. We performed logistic regression analysis and obtained eight regression equations for each ADR. We converted these to ADR estimate equations for predicting the likelihood of ADRs. We randomly selected 50 cases with non-individual ADRs from the Case Reports of Adverse Drug Reactions and Poisoning Information System (CARPIS) database of over 65000 case reports of ADRs, and assigned these cases to a validation case group. We then calculated the predictive probability for 50 cases using the eight estimate equations for each ADR. The highest probability for each ADR was set as the probability of each ADR. If the probability was over 50％, the case was interpreted as ADR-positive. We calculated and evaluated the sensitivity, speciˆcity, and positive likelihood ratio of this system. Sensitivity of the estimate equations for muscle disorder and interstitial pneumonia were 90％. Speciˆcity and positive likelihood ratios of estimate equations for renal damage, interstitial pneumonia and leucopenia were 80％ and 5, respectively. Our estimate equations thus showed high validity, and are therefore helpful for the prevention or early detection of ADRs.