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BackgroundPain is a common condition with a significant physical, psychosocial, and economic impact. Due to enormous progress in mobile device technology as well as the increase in smartphone ownership in the general population, mobile apps can be used to monitor patients with pain and support them in pain management. ObjectiveThe aim of this review was to assess the efficacy of smartphone or computer tablet apps in the management of patients with pain. MethodsIn December 2017, a literature search was performed in the following databases: MEDLINE, EMBASE, CINAHL, Cochrane, and PsycINFO. In addition, a bibliography search was conducted. We included studies with at least 20 participants per arm that evaluated the effects of apps on smartphones or computer tablets on improvement in pain. ResultsA total of 15 studies with 1962 patients met the inclusion criteria. Of these, 4 studies examined the effect of mobile apps on pain management in an in-clinic setting and 11 in an out-clinic setting. The majority of the original studies reported beneficial effects of the use of a pain app. Severity of pain decreased in most studies where patients were using an app compared with patients not using an app. Other outcomes, such as worst pain or quality of life showed improvements in patients using an app. Due to heterogeneity between the original studies—patient characteristics, app content, and study setting—a synthesis of the results by statistical methods was not performed. ConclusionsApps for pain management may be beneficial for patients, particularly in an out-clinic setting. Studies have shown that pain apps are workable and well liked by patients and health care professionals. There is no doubt that in the near future, mobile technologies will develop further. Medicine could profit from this development as indicated by our results, but there is a need for more scientific inputs. It is desirable to know which elements of apps or additional devices and tools may improve usability and help patients in pain management.

BackgroundC-reactive protein (CRP) is increasingly being included in the diagnostic work-up for community-acquired pneumonia in primary care. Its added diagnostic value beyond signs and symptoms, however, remains unclear. We conducted a meta-analysis of individual patient data to quantify the added value of CRP measurement.MethodsWe included studies of the diagnostic accuracy of CRP in adult outpatients with suspected lower respiratory tract infection. We contacted authors of eligible studies for inclusion of data and for additional data as needed. The value of adding CRP measurement to a basic signs-and-symptoms prediction model was assessed. Outcome measures were improvement in discrimination between patients with and without pneumonia in primary care and improvement in risk classification, both within the individual studies and across studies.ResultsAuthors of 8 eligible studies (n = 5308) provided their data sets. In all of the data sets, discrimination between patients with and without pneumonia improved after CRP measurement was added to the prediction model (extended model), with a mean improvement in the area under the curve of 0.075 (range 0.02-0.18). In a hypothetical cohort of 1000 patients, the proportion of patients without pneumonia correctly classified at low risk increased from 28% to 36% in the extended model, and the proportion with pneumonia correctly classified at high risk increased from 63% to 70%. The number of patients with pneumonia classified at low risk did not change (n = 4). Overall, the proportion of patients assigned to the intermediate-risk category decreased from 56% to 51%.InterpretationAdding CRP measurement to the diagnostic work-up for suspected pneumonia in primary care improved the discrimination and risk classification of patients. However, it still left a substantial group of patients classified at intermediate risk, in which clinical decision-making remains challenging.

BackgroundPneumonia remains difficult to diagnose in primary care. Prediction models based on signs and symptoms (S&S) serve to minimize the diagnostic uncertainty. External validation of these models is essential before implementation into routine practice. In this study all published S&S models for prediction of pneumonia in primary care were externally validated in the individual patient data (IPD) of previously performed diagnostic studies.Methods and findingsS&S models for diagnosing pneumonia in adults presenting to primary care with lower respiratory tract infection and IPD for validation were identified through a systematical search. Six prediction models and IPD of eight diagnostic studies (N total = 5308, prevalence pneumonia 12%) were included. Models were assessed on discrimination and calibration. Discrimination was measured using the pooled Area Under the Curve (AUC) and delta AUC, representing the performance of an individual model relative to the average dataset performance. Prediction models by van Vugt et al. and Heckerling et al. demonstrated the highest pooled AUC of 0.79 (95% CI 0.74-0.85) and 0.72 (0.68-0.76), respectively. Other models by Diehr et al., Singal et al., Melbye et al., and Hopstaken et al. demonstrated pooled AUCs of 0.65 (0.61-0.68), 0.64 (0.61-0.67), 0.56 (0.49-0.63) and 0.53 (0.5-0.56), respectively. A similar ranking was present based on the delta AUCs of the models. Calibration demonstrated close agreement of observed and predicted probabilities in the models by van Vugt et al. and Singal et al., other models lacked such correspondence. The absence of predictors in the IPD on dataset level hampered a systematical comparison of model performance and could be a limitation to the study.ConclusionsThe model by van Vugt et al. demonstrated the highest discriminative accuracy coupled with reasonable to good calibration across the IPD of different study populations. This model is therefore the main candidate for primary care use.