Rationale: Current technical standards advocate using Generalized, Additive Models of Location, Scale, and Shape (GAMLSS) for lung function reference equations. These equations are complicated and require supplementary spline tables. Objective: (1) To demonstrate that segmented (piecewise) linear regression (SLR) yields prediction accuracies similar to GAMLSS in pulmonary function diagnostics. (2) To determine the agreement between both SLR and GAMLSS. Methods: The NHANES 2007-2012 database was utilized to construct spirometric reference equations for FEV1, FVC, and FEV1/FVC using both SLR and GAMLSS modeling techniques. K-fold cross-validation was used to provide the 95% confidence interval (CI) of the root-mean-square error (RMSE) as an indicator of prediction accuracy. Additionally, agreement was assessed between the two modeling techniques in classifying spirometric patterns (standard, airflow obstruction, restrictive, or mixed disorder) using an unweighted kappa statistic. Results: The RMSE values for FEV1, FVC, and FEV1/FVC and correlation coefficients between predicted values and test data were similar between the two techniques. Agreement in classifying spirometric patterns between the two techniques ranged from 0.78 to 0.80 (95 % CI). Conclusions: The findings suggest that simple linear regression for FEV1/FVC and SLR for FEV1 and FVC offer prediction accuracies on par with GAMLSS while being more straightforward, parsimonious, and accessible to a broader audience in the field of pulmonary function diagnostics.

This study aimed to evaluate discordance, binary classification, and model fit between race‐predicted and race‐neutral spirometry prediction equations. Spirometry data from 9506 patients (18–95 years old) self‐identifying as White, Black, or Hispanic were analyzed, focusing on the lower limit of normal (LLN). Best‐fit prediction equations were developed from 3771 patients with normal spirometry, using Bayesian Information Criterion (BIC) to compare models with and without race as a covariate. Results showed that including race as a covariate improved model fit, reducing BIC by at least ten units compared to Race‐Neutral equations. Discordance between race‐specific and race‐neutral equations for detecting airway obstruction and restrictive spirometry patterns ranged from 4% to 13%. Using race‐neutral equations resulted in false discovery rates (FDR) of 14% for Hispanics and 45% for Blacks and false negative rates (FNR) of 21% for Hispanics and 27% for Blacks in diagnosing airway obstruction. These findings indicate that removing race as a covariate in spirometry equations increases FDR and FNR, leading to higher misclassification rates. The 4%–13% discordance in interpreting airway obstruction and restrictive patterns has significant clinical implications, underscoring the need for careful consideration in developing spirometry reference equations.

Pulmonary complications remain a significant challenge for COVID-19 survivors, necessitating advanced diagnostic approaches for long-term assessment. We present a curated, open-access dataset of pulmonary function measurements—including nitric oxide (DLNO) and carbon monoxide (DLCO) diffusing capacities—in 572 post–COVID-19 patients and 72 healthy controls (filtered from an original cohort of 726 survivors and 126 controls). Collected across eight international centres, the data include demographics, spirometry, lung volumes, and 5–6 s single-breath DLNO5s, DLCO5s, and alveolar volume (VA5s). Missing values for total lung capacity were imputed, and low-quality or system-specific (Hyp’Air Compact) measurements were excluded in the filtered dataset. A third subset (333 patients, 54 controls) links these measurements to dyspnoea severity (mMRC scale) for correlation and proportional odds analyses. This resource underpins predictive modeling of post–COVID pulmonary impairment via summed z-scores (DLNO + DLCO) and aims to accelerate validation of NO-CO diagnostics. The freely accessible datasets are provided in both SPSS (.sav) and .csv formats at the Mendeley Data Cloud-based repository and includes nominal, ordinal, and scalar data.

Rationale: Pleural fluid pH is a simple, accessible measurement that can be valuable in evaluating pleural effusions. While prior studies have assessed typical pH values for various effusion causes, few have established pleural fluid pH reference ranges based on large datasets. In this study, we aimed to develop reference ranges for pleural fluid pH across several common etiologies, including malignancy, uncomplicated parapneumonic effusions, empyema, volume overload, and hepatic hydrothorax. Methods: We conducted a retrospective analysis of thoracentesis cases at UC Davis Medical Center between February 2011 and April 2024. Cases were excluded if they had incomplete data or errors, specifically missing values for pleural fluid pH, LDHfluid, TCfluid, or time-to-analysis data. To compare medians among the five etiologies, we applied an independent samples median test adjusted with Bonferroni correction. Additionally, we used an independent samples Kruskal-Wallis test to examine pleural fluid pH distribution differences across the groups. We calculated reference intervals for each etiology using a weighted average, with statistical significance defined as p < 0.05. Results: We included 407 patients (43% female) across the five etiologies. The collection to analyse time ranged from 5 to 60 minutes. Median pH values varied significantly among the groups. Pairwise comparisons indicated that pleural fluid pH values were significantly different between groups, except for malignancy vs. parapneumonic effusions, and the volume overload vs hepatic hydrothorax groups. Reference intervals for each etiology are detailed in Table 1. Table 1: Reference intervals using a weighted average. Mean (SD) age = 63.5 (15) years old; BMI = 25.5 (5.7) kg/m2. Conclusions: This study provides reference ranges for pleural fluid pH across common effusion etiologies, aiding clinicians in making more precise and timely diagnoses. Notably, no significant pH difference was found between malignant and parapneumonic effusions, as well as between volume overload and hepatic hydrothorax. When effusion pH falls within these overlapping ranges, further diagnostic testing is recommended to identify the underlying etiology accurately.

We aimed to examine the preparedness of recent gynecologic oncology fellowship graduates for independent practice.We conducted a web-based survey study using REDCap targeting Society of Gynecologic Oncology (SGO) members who graduated gynecologic oncology fellowship within the last six years. The survey included 52 items assessing fellowship training experiences, level of comfort in performing core gynecologic oncology surgical procedures and administering cancer-directed therapies. Questions also addressed factors driving participants’ selection of fellowship programs, educational experience, research and preparedness for independent practice. A total of 296 participants were invited to complete the survey. Response rate was 42% with n=124 completed surveys included for analysis. The highest ranked factor for fellowship selection was fit with program 36% (n=45). Upon completing fellowship, most were uncomfortable performing ureteral conduit formation 84% (n=103), ureteroneocystostomy 77% (n=94), exenteration 68% (n=83), splenectomy 67% (n=83) and lower anterior resection 41% (n=51). Most were comfortable managing intraoperative complications 85% (n=104) and standard cancer staging procedures (range: 61%-99%). Majority were comfortable providing cancer directed therapies with chemotherapy 99% (n=123), immunotherapy 84% (n=104), and poly ADP-ribose polymerase (PARP) inhibitors 97% (n=120). Upon completing fellowship, 77% (n=95) report having mentorship that met their expectations during fellowship and 94% (n=116) felt they were ready for independent practice. Majority of fellowship graduates were prepared for independent practice and felt comfortable performing routine surgical procedures and cancer directed treatment. However, most are not comfortable with ultra-radical gynecologic oncology procedures. Maximizing surgical opportunities during fellowship training and acquiring early career mentorship may help.

BACKGROUND:Infants with hypoxic-ischemic encephalopathy are often treated with therapeutic hypothermia and high-frequency ventilation. Fluctuations in PaCO2 during therapeutic hypothermia are associated with poor neurodevelopmental outcomes. Transcutaneous CO2 monitors offer a noninvasive estimate of PaCO2 represented by transcutaneously measured partial pressure of carbon dioxide (PtcCO2 ). We aimed to assess the precision between PtcCO2 and PaCO2 values in neonates undergoing therapeutic hypothermia. METHODS:This was a retrospective chart review of 10 neonates who underwent therapeutic hypothermia requiring respiratory support over 2 y. A range of 2-27 simultaneous PtcCO2 and PaCO2 pairs of measurements per neonate were analyzed via linear mixed models and a Bland-Altman plot for multiple observations per neonate. RESULTS:A linear mixed-effect model demonstrated that PtcCO2 and PaCO2 (controlling for sex) were similar. The 95% CI of the mean difference ranged from -2.3 to 5.7 mm Hg (P = .41). However, precision was poor as the PtcCO2 ranged from > 18 mm Hg to < 13 mm Hg than PaCO2 values for 95% of observations. CONCLUSIONS:The neonates' PtcCO2 was as much as 18 mm Hg higher to 13 mm Hg lower than the PaCO2 95% of the time. Transcutaneous CO2 monitoring may not be a good trending tool, nor is it appropriate for estimating PaCO2 in patients undergoing therapeutic hypothermia.

This observational longitudinal study was conducted at the Long-term follow-up COVID-19 Clinic in Mérida, Mexico, from March to August 2021. A total of 100 patients hospitalized for severe COVID-19 were enrolled. Inclusion criteria required participants to be adults over 18, recovering from severe COVID-19 as defined by the World Health Organization (oxygen saturation below 90 %, severe pneumonia, or signs of severe respiratory distress). Exclusion criteria included pneumonia from non-SARS-CoV-2 causes, mild or moderate COVID-19, or a single follow-up evaluation. Pulmonary function tests were conducted at approximately 100 and 400 days after diagnosis. The dataset includes 82 patients with baseline and follow-up spirometry, pulmonary diffusing capacity and alveolar volume. Morbidity history and fibrosis scores from high-resolution CT scans were also obtained. Finally, fitted z-scores for spirometry and pulmonary diffusing capacity were acquired from established reference equations. The freely accessible data (Version 4) is provided in both SPSS (.sav) and .csv format. at the Mendeley Data cloud-based repository and includes nominal data, ordinal data, and scalar data.

Background Heart failure (HF) is a chronic condition in which the heart does not pump enough blood to meet the body's demands. Diffusing capacity of the lung for nitric oxide (D-LNO) and carbon monoxide (D-LCO) may be used to classify patients with HF, as D-LNO and D-LCO are lung function measurements that reflect pulmonary gas exchange. Our objectives were to determine 1) if D-LNO added to D-LCO testing predicts HF better than D-LCO alone and 2) whether the binary classification of HF is better when D-LNO z-scores are combined with D-LCO z-scores than using D-LCO z-scores alone. Methods This was a retrospective secondary data analysis in 140 New York Heart Association Class II HF patients (ejection fraction <40%) and 50 patients without HF. z-scores for D-LNO, D-LCO and D-LNO+D-LCO were created from reference equations from three articles. The model with the lowest Bayesian Information Criterion was the best predictive model. Binary HF classification was evaluated with the Matthews Correlation Coefficient (MCC). Results The top two of 12 models were combined z-score models. The highest MCC (0.51) was from combined z-score models. At most, only 32% of the variance in the odds of having HF was explained by combined z-scores. Conclusions Combined z-scores explained 32% of the variation in the likelihood of an individual having HF, which was higher than models using D-LNO or D-LCO z-scores alone. Combined z-score models had a moderate ability to classify patients with HF. We recommend using the NO-CO double diffusion technique to assess gas exchange impairment in those suspected of HF.

Objectives This study aimed to evaluate pulmonary diffusing capacity for nitric oxide (DLNO) and pulmonary diffusing capacity for carbon monoxide (DLCO) in Mexican Hispanics born and raised at 2240 m altitude (midlanders) compared with those born and raised at sea level (lowlanders). It also aimed to assess the effectiveness of race-specific reference equations for pulmonary diffusing capacity (white people vs Mexican Hispanics) in minimising root mean square errors (RMSE) compared with race-neutral equations.Methods DLNO, DLCO, alveolar volume (VA) and gas transfer coefficients (KNO and KCO) were measured in 392 Mexican Hispanics (5 to 78 years) and compared with 1056 white subjects (5 to 95 years). Reference equations were developed using segmented linear regression (DLNO, DLCO and VA) and multiple linear regression (KNO and KCO) and validated with Least Absolute Shrinkage and Selection Operator. RMSE comparisons between race-specific and race-neutral models were conducted using repeated k-fold cross-validation and random forests.Results Midlanders exhibited higher DLCO (mean difference: +4 mL/min/mm Hg), DLNO (mean difference: +7 mL/min/mm Hg) and VA (mean difference: +0.17 L) compared with lowlanders. The Bayesian information criterion favoured race-specific models and excluding race as a covariate increased RMSE by 61% (DLNO), 18% (DLCO) and 4% (KNO). RMSE values for VA and KCO were comparable between race-specific and race-neutral models. For DLCO and DLNO, race-neutral equations resulted in 3% to 6% false positive rates (FPRs) in Mexican Hispanics and 20% to 49% false negative rates (FNRs) in white subjects compared with race-specific equations.Conclusions Mexican Hispanics born and raised at 2240 m exhibit higher DLCO and DLNO compared with lowlanders. Including race as a covariate in reference equations lowers the RMSE for DLNO, DLCO and KNO and reduces FPR and FNR compared with race-neutral models. This study highlights the need for altitude-specific and race-specific reference equations to improve pulmonary function assessments across diverse populations.

Abstract The long‐term effects of COVID‐19 on lung function are not understood, especially for periods extending beyond 1 year after infection. This observational, longitudinal study investigated lung function in Mexican Hispanics who experienced severe COVID‐19, focusing on how the length of recovery affects lung function improvements. At a specialized COVID‐19 follow‐up clinic in Yucatan, Mexico, lung function and symptoms were assessed in patients who had recovered from severe COVID‐19. We used z‐scores, and Wilcoxon's signed rank test to analyse changes in lung function over time. Lung function was measured twice in 82 patients: the first and second measurements were taken a median of 94 and 362 days after COVID‐19 diagnosis, respectively. Initially, 61% of patients exhibited at least one of several pulmonary function abnormalities (lower limit of normal = –1.645), which decreased to 22% of patients by 390 days post‐recovery. Considering day‐to‐day variability in lung function, 68% of patients showed improvement by the final visit, while 30% had unchanged lung function from the initial assessment. Computed tomography (CT) scans revealed ground‐glass opacities in 33% of patients. One year after infection, diffusing capacity of the lungs for carbon monoxide z‐scores accounted for 30% of the variation in CT fibrosis scores. There was no significant correlation between the length of recovery and improvement in lung function based on z‐scores. In conclusion, 22% of patients who recovered from severe COVID‐19 continued to show at least one lung function abnormality 1 year after recovery, indicating a prolonged impact of COVID‐19 on lung health.

Generalized Additive Models for Location, Scale, and Shape (GAMLSS) are widely used for developing spirometric reference equations but are often complex, requiring additional spline tables. This study explores the potential of Segmented (piecewise) Linear Regression as an alternative, comparing its predictive accuracy to GAMLSS and examining the agreement between the two methods. Spirometry data from nearly 16,600 patients, deemed Grade "A" and "B" acceptable from the NHANES 2007-2012 dataset, was analyzed. The dataset includes both nominal and scalar variables. Reference equations for forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and the ratio (FEV1/FVC) were generated using GAMLSS (FEV1, FVC, FEV1/FVC), Segmented Linear Regression (FEV1, FVC) and multiple linear regression (FEV1/FVC). K-fold cross-validation was employed to compare prediction accuracy, using root-mean-square error (RMSE) and correlation coefficients. Agreement in classifying spirometric patterns (i.e. airway obstruction, restrictive spirometry pattern, mixed obstructive and restrictive disorder) was evaluated with the kappa statistic. This study uniquely compares the models by incorporating the lower limit of normal (LLN) using fitted z-scores of -1.645 or -1.96. The dataset is publicly available in SPSS (.sav) and .csv formats through the Mendeley Data repository.

Abstract Objectives Human blood gas stability data is limited to small sample sizes and questionable statistical techniques. We sought to determine the stability of blood gases under room temperature and slushed iced conditions in patients using survival analyses. Methods Whole blood samples from ∼200 patients were stored in plastic syringes and kept at room temperature (22–24 °C) or in slushed ice (0.1–0.2 °C) before analysis. Arterial and venous pO2 (15–150 mmHg), pCO2 (16–72 mmHg), pH (6.73–7.52), and the CO-oximetry panel [total hemoglobin (5.4–19.3 g/dL), percentages of oxyhemoglobin (O2Hb%, 20–99%), carboxyhemoglobin (COHb, 0.1–5.4%) and methemoglobin (MetHb, 0.2–4.6%)], were measured over 5-time points. The Royal College of Pathologists of Australasia’s (RCPA’s) criteria determined analyte instability. Survival analyses identified storage times at which 5% of the samples for various analytes became unstable. Results COHb and MetHb were stable up to 3 h in slushed ice and at room temperature; pCO2, pH was stable at room temperature for about 60 min and 3 h in slushed ice. Slushed ice shortened the storage time before pO2 became unstable (from 40 to 20 min), and the instability increased when baseline pO2 was ≥60 mmHg. The storage time for pO2, pCO2, pH, and CO-oximetry, when measured together, were limited by the pO2. Conclusions When assessing pO2 in plastic syringes, samples kept in slushed ice harm their stability. For simplicity’s sake, the data support storage times for blood gas and CO-oximetry panels of up to 40 min at room temperature if following RCPA guidelines.

Background Severe acute respiratory syndrome caused by a coronavirus (SARS-CoV-2) is responsible for the COVID-19 disease pandemic that began in Wuhan, China, in December 2019. Since then, nearly seven million deaths have occurred worldwide due to COVID-19. Mexicans are especially vulnerable to the COVID-19 pandemic as Mexico has nearly the worst observed case-fatality ratio (4.5%). As Mexican Latinos represent a vulnerable population, this study aimed to determine significant predictors of mortality in Mexicans with COVID-19 who were admitted to a large acute care hospital. Methods In this observational, cross-sectional study, 247 adult patients were consecutively admitted to a third-level referral center in Yucatan, Mexico, from March 1st, 2020, to August 31st, 2020, with COVID-19-related symptoms, participated in this study. Lasso logistic and binary logistic regression were used to identify clinical predictors of death. Results After a hospital stay of about eight days, 146 (60%) patients were discharged; however, 40% died by the twelfth day (on average) after hospital admission. Out of 22 possible predictors, five crucial predictors of death were found, ranked by the most to least important: (1) needing to be placed on a mechanical ventilator, (2) reduced platelet concentration at admission, (3) increased derived neutrophil to lymphocyte ratio, (4) increased age, and (5) reduced pulse oximetry saturation at admission. The model revealed that these five variables shared ~83% variance in outcome. Conclusion Of the 247 Mexican Latinos patients admitted with COVID-19, 40% died 12  days after admission. The patients’ need for mechanical ventilation (due to severe illness) was the most important predictor of mortality, as it increased the odds of death by nearly 200-fold.

Article The stability of pleural fluid pH under slushed ice and room temperature conditions was published on January 1, 2023 in the journal Clinical Chemistry and Laboratory Medicine (CCLM) (volume 61, issue 1).

Background: There are few studies have assessed lung function in Hispanic subjects recovering from mild COVID-19. Therefore, we examined the prevalence of impaired pulmonary diffusing capacity for carbon monoxide (DLCO) as defined by values below the lower limit of normal (< LLN, < 5 th percentile) in Hispanics recovering from mild COVID-19. We also examined the prevalence of a restrictive spirometric pattern as defined by the ratio of forced expiratory volume in 1 s (FEV 1 ) to forced vital capacity (FVC) being ≥ LLN with the FVC being < LLN. Finally, we wanted to examine factors that cause the prevalence of an impaired DLCO to vary between studies. Methods: In this observational study, adult patients (n = 146) with mild COVID-19 were recruited from a Long-term follow-up COVID-19 clinic in Yucatan, Mexico between March, and August 2021. Spirometry, DLCO, and self-reported signs/symptoms were recorded 34 ± 4 days after diagnosis. Results: At post-evaluation, 20% and 30% patients recovering from COVID-19 were classified as having a restrictive spirometric pattern and impaired DLCO, respectively; 13% had both. The most prevalent reported symptoms were fatigue (73%), persistent cough (43%), shortness of breath (42%) and a blocked/runny nose (36%). Increased age, a blocked/runny nose, excessive night sweats, and a restrictive spirometric pattern increased probability of having an impaired DLCO. The proportion of patients with previous mild COVID-19 who had impaired DLCO increased by 12% when the definition of impaired DLCO was < 80% predicted instead of < LLN. Having severe (compared to mild) COVID-19 increased the percentage of those with impaired DLCO by 20%. Conclusions: One-third of patients with mild COVID-19 have impaired DLCO thirty-four days post-diagnosis. One-fifth of patients have a restrictive spirometric pattern. The criteria that define impaired DLCO and the severity of COVID-19 disease affects the proportion of those with impaired DLCO at follow-up.

Several anecdotal reports suggest that sex before competition can affect performance. Our objective was to perform a systematic review and meta-analysis to determine whether athletic performance or some physical fitness measure is affected by prior sexual activity. Web of Science (all databases) and Google Scholar were used to identify studies from which adult healthy subjects were included. As all studies were crossover trials, an inverse variance statistical method with random effects was used to minimize the uncertainty of the pooled effect estimate. Bias was assessed via the revised Cochrane Risk of Bias tool (RoB 2) with a "per protocol" analysis. Nine crossover studies (133 subjects, 99% male) were used in this meta-analysis. All those studies did not examine athletic performance per se, but all studies assessed one or more physical fitness parameters. The RoB 2 suggested that overall, there were some concerns with bias. As there was moderate heterogeneity amongst the different outcomes (Tau 2 = 0.02, Chi-square = 17.2, df = 8, p = 0.03, I 2 = 54%), a random-effects model was used. The results neither favored abstinence nor sexual activity before a physical fitness test [standardized mean difference = 0.03 (− 0.10 to 0.16), Z = 0.47, p = 0.64, where a negative standardized mean difference favors abstinence, and a positive standardized mean difference favors sexual activity]. The results demonstrate that sexual activity within 30 min to 24 h before exercise does not appear to affect aerobic fitness, musculoskeletal endurance, or strength/power.

Background Data on the stability of whole blood electrolytes is limited to small sample sizes. We sought to determine the stability of whole blood electrolytes under room temperature and slushed iced conditions in human patients at a major hospital center. Methods Whole blood samples were obtained from 203 patients hospitalized for various pathophysiological conditions. Electrolyte concentrations of sodium, potassium [K+], ionized calcium, and chloride were measured at 5 different timepoints spanning 3 h. Samples were stored at room temperature (22-24 degrees C) or under slushed ice conditions (0.1-0.2 degrees C) before analysis. Results Under both conditions, sodium, ionized calcium, and chloride did not show a measurable change up to 109 min compared to baseline; however, the mean increase in [K+] over 138 min of storage in slushed ice was 0.0032 (0.0021 [5th percentile] to 0.0047 [95th percentile]) mmol/L/min (adjusted R-2 = 0.62, P < 0.001). Five percent of the specimens demonstrated a >= 0.3 mmol/L change in [K+] from baseline after 67 min of storage in slushed ice. In contrast, 1% of the specimens stored at room temperature showed the same change at the same timepoint. Conclusions Whole blood sodium, [K+], ionized calcium, and chloride concentrations remain stable for at least 109 min at room temperature. However, whole blood specimens stored in slushed ice for not more than 67 min exhibit a 5% probability that the [K+] concentration will increase by at least 0.3 mmol/L compared to baseline. The other analytes do not destabilize for up to 178 min of slushed ice storage.

Purpose To determine whether generalised additive models of location, scale and shape (GAMLSS) developed for pulmonary diffusing capacity are superior to segmented (piecewise) regression models, and to update reference equations for pulmonary diffusing capacity for carbon monoxide (DLCO) and nitric oxide (DLNO), which may be affected by the equipment used for its measurement. Methods Data were pooled from five studies that developed reference equations for DLCO and DLNO (n=530 F/546 M; 5–95 years old, body mass index 12.4–39.0 kg/m 2 ). Reference equations were created for DLCO and DLNO using both GAMLSS and segmented linear regression. Cross-validation was applied to compare the prediction accuracy of the two models as follows: 80% of the pooled data were used to create the equations, and the remaining 20% was used to examine the fit. This was repeated 100 times. Then, the root-mean-square error was compared between both models. Results In males, GAMLSS models were 7% worse to 3% better compared to segmented regression for DLCO and DLNO. In females, GAMLSS models were 2% worse to 5% better compared to segmented linear regression for DLCO and DLNO. The Hyp'Air Compact measured DLNO and alveolar volume (VA) that was approximately 16–20 mL/min/mm Hg and 0.2–0.4 L higher, respectively, compared to the Jaeger MasterScreen Pro. The measured DLCO was similar between devices after controlling for altitude. Conclusions For the development of pulmonary function reference equations, we propose that segmented linear regression can be used instead of GAMLSS due to its simplicity, especially when the predictive accuracy is similar between the two models, overall.