Does This Patient Have Acute Mountain Sickness?: The Rational Clinical Examination Systematic Review.

Acute mountain sickness (AMS) affects more than 25% of individuals ascending to 3500 m (11 500 ft) and more than 50% of those above 6000 m (19 700 ft). AMS may progress from nonspecific symptoms to life-threatening high-altitude cerebral edema in less than 1% of patients. It is not clear how to best diagnose AMS. To systematically review studies assessing the accuracy of AMS diagnostic instruments, including the visual analog scale (VAS) score, which quantifies the overall feeling of sickness at altitude (VAS[O]; various thresholds), Acute Mountain Sickness-Cerebral score (AMS-C; ≥0.7 indicates AMS), and the clinical functional score (CFS; ≥2 indicates AMS) compared with the Lake Louise Questionnaire Score (LLQS; score of ≥5). Searches of MEDLINE and EMBASE from inception to May 2017 identified 1245 publications of which 91 were suitable for prevalence analysis (66 944 participants) and 14 compared at least 2 instruments (1858 participants) using a score of 5 or greater on the LLQS as a reference standard. To determine the prevalence of AMS for establishing the pretest probability of AMS, a random-effects meta-regression was performed based on the reported prevalence of AMS as a function of altitude. AMS prevalence, likelihood ratios (LRs), sensitivity, and specificity of screening instruments. The final analysis included 91 articles (comprising 66 944 study participants). Altitude predicted AMS and accounted for 28% of heterogeneity between studies. For each 1000-m (3300-ft) increase in altitude above 2500 m (8200 ft), AMS prevalence increased 13% (95% CI, 9.5%-17%). Testing characteristics were similar for VAS(O), AMS-C, and CFS vs a score of 5 or greater on the LLQS (positive LRs: range, 3.2-8.2; P = .22 for comparisons; specificity range, 67%-92%; negative LRs: range, 0.30-0.36; P = .50 for comparisons; sensitivity range, 67%-82%). The CFS asks a single question: "overall if you had any symptoms, how did they affect your activity (ordinal scale 0-3)?" For CFS, moderate to severe reduction in daily activities had a positive LR of 3.2 (95% CI, 1.4-7.2) and specificity of 67% (95% CI, 37%-97%); no reduction to mild reduction in activities had a negative LR of 0.30 (95% CI, 0.22-0.39) and sensitivity of 82% (95% CI, 77%-87%). The prevalence of acute mountain sickness increases with higher altitudes. The visual analog scale for the overall feeling of sickness at altitude, Acute Mountain Sickness-Cerebral, and clinical functional score perform similarly to the Lake Louise Questionnaire Score using a score of 5 or greater as a reference standard. In clinical and travel settings, the clinical functional score is the simplest instrument to use. Clinicians evaluating high-altitude travelers who report moderate to severe limitations in activities of daily living (clinical functional score ≥2) should use the Lake Louise Questionnaire Score to assess the severity of acute mountain sickness

A methodological framework to distinguish spectrum effects from spectrum biases and to assess diagnostic and screening test accuracy for patient populations: Application to the Papanicolaou cervical cancer smear test

<p>Abstract</p> <p>Background</p> <p>A spectrum effect was defined as differences in the sensitivity or specificity of a diagnostic test according to the patient's characteristics or disease features. A spectrum effect can lead to a spectrum bias when subgroup variations in sensitivity or specificity also affect the likelihood ratios and thus post-test probabilities. We propose and illustrate a methodological framework to distinguish spectrum effects from spectrum biases.</p> <p>Methods</p> <p>Data were collected for 1781 women having had a cervical smear test and colposcopy followed by biopsy if abnormalities were detected (the reference standard). Logistic models were constructed to evaluate both the sensitivity and specificity, and the likelihood ratios, of the test and to identify factors independently affecting the test's characteristics.</p> <p>Results</p> <p>For both tests, human papillomavirus test, study setting and age affected sensitivity or specificity of the smear test (spectrum effect), but only human papillomavirus test and study setting modified the likelihood ratios (spectrum bias) for clinical reading, whereas only human papillomavirus test and age modified the likelihood ratios (spectrum bias) for "optimized" interpretation.</p> <p>Conclusion</p> <p>Fitting sensitivity, specificity and likelihood ratios simultaneously allows the identification of covariates that independently affect diagnostic or screening test results and distinguishes spectrum effect from spectrum bias. We recommend this approach for the development of new tests, and for reporting test accuracy for different patient populations.</p

Effects of hospital facilities on patient outcomes after cancer surgery: an international, prospective, observational study

Background Early death after cancer surgery is higher in low-income and middle-income countries (LMICs) compared with in high-income countries, yet the impact of facility characteristics on early postoperative outcomes is unknown. The aim of this study was to examine the association between hospital infrastructure, resource availability, and processes on early outcomes after cancer surgery worldwide.Methods A multimethods analysis was performed as part of the GlobalSurg 3 study-a multicentre, international, prospective cohort study of patients who had surgery for breast, colorectal, or gastric cancer. The primary outcomes were 30-day mortality and 30-day major complication rates. Potentially beneficial hospital facilities were identified by variable selection to select those associated with 30-day mortality. Adjusted outcomes were determined using generalised estimating equations to account for patient characteristics and country-income group, with population stratification by hospital.Findings Between April 1, 2018, and April 23, 2019, facility-level data were collected for 9685 patients across 238 hospitals in 66 countries (91 hospitals in 20 high-income countries; 57 hospitals in 19 upper-middle-income countries; and 90 hospitals in 27 low-income to lower-middle-income countries). The availability of five hospital facilities was inversely associated with mortality: ultrasound, CT scanner, critical care unit, opioid analgesia, and oncologist. After adjustment for case-mix and country income group, hospitals with three or fewer of these facilities (62 hospitals, 1294 patients) had higher mortality compared with those with four or five (adjusted odds ratio [OR] 3.85 [95% CI 2.58-5.75]; p&lt;0.0001), with excess mortality predominantly explained by a limited capacity to rescue following the development of major complications (63.0% vs 82.7%; OR 0.35 [0.23-0.53]; p&lt;0.0001). Across LMICs, improvements in hospital facilities would prevent one to three deaths for every 100 patients undergoing surgery for cancer.Interpretation Hospitals with higher levels of infrastructure and resources have better outcomes after cancer surgery, independent of country income. Without urgent strengthening of hospital infrastructure and resources, the reductions in cancer-associated mortality associated with improved access will not be realised

Diagnostic accuracy of spirometry in primary care.

Contains fulltext : 80404.pdf (publisher's version ) (Open Access)BACKGROUND: To evaluate the sensitivity, specificity and predictive values of spirometry for the diagnosis of chronic obstructive pulmonary disease (COPD) and asthma in patients suspected of suffering from an obstructive airway disease (OAD) in primary care. METHODS: Cross sectional diagnostic study of 219 adult patients attending 10 general practices for the first time with complaints suspicious for OAD. All patients underwent spirometry and structured medical histories were documented. All patients received whole-body plethysmography (WBP) in a lung function laboratory. The reference standard was the Tiffeneau ratio (FEV1/VC) received by the spirometric maneuver during examination with WBP. In the event of inconclusive results, bronchial provocation was performed to determine bronchial hyper-responsiveness (BHR). Asthma was defined as a PC20 fall after inhaling methacholine concentration < or = 16 mg/ml. RESULTS: 90 (41.1%) patients suffered from asthma, 50 (22.8%) suffered from COPD, 79 (36.1%) had no OAD. The sensitivity for diagnosing airway obstruction in COPD was 92% (95%CI 80-97); specificity was 84% (95%CI 77-89). The positive predictive value (PPV) was 63% (95%CI 51-73); negative predictive value (NPV) was 97% (95%CI 93-99). The sensitivity for diagnosing airway obstruction in asthma was 29% (95%CI 21-39); specificity was 90% (95%CI 81-95). PPV was 77% (95%CI 60-88); NPV was 53% (95%CI 45-61). CONCLUSION: COPD can be estimated with high diagnostic accuracy using spirometry. It is also possible to rule in asthma with spirometry. However, asthma can not be ruled out only using spirometry. This diagnostic uncertainty leads to an overestimation of asthma presence. Patients with inconclusive spirometric results should be referred for nitric oxide (NO) - measurement and/or bronchial provocation if possible to guarantee accurate diagnosis